JP2014023606A - Image processing device and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing technology for stably improving extraction accuracy at a high speed when extracting an object area from a moving image.SOLUTION: An image processing device 3 includes: a shape model fitting part 120 for determining a fitting function for approximating a boundary of a lung field area on a frame image by performing fitting processing for fitting a shape model representing a general shape of the lung field area of the frame image, and sequentially acquiring lung field area boundary information obtained by reflecting at least the fitting function; and a shape model control part 130 for performing shape model change processing for sequentially changing a shape model used in the fitting processing to the frame image along a time direction. A fitting function F is composed of the prescribed number of functions, and a shape model M is a model for determining the fitting function F, and includes at least one piece of information in the prescribed number and the allowable range of a degree of each of the functions.

Description

  The present invention relates to an image processing technique for extracting a target region from a dynamic image obtained by photographing a human body or an animal body.

  In a medical field, various examinations and diagnoses are performed by photographing an affected part included in an internal organ, a skeleton, or the like using X-rays or the like. In recent years, it has become possible to relatively easily acquire a moving image that captures the motion of an affected area using X-rays or the like by applying digital technology.

  In this method, a solid-state image sensor such as a flat panel detector (FPD) can be used to capture a dynamic image of a subject area including the target area. It has become possible to carry out diagnosis by motion analysis of areas and the like. In medical images, information useful for diagnosis of the respiratory system and circulatory system can be obtained by analyzing target regions such as organs and bones in the time direction, so the target region or boundary can be extracted accurately. There is a need to.

  By the way, as a method for extracting a target region from a still image, for example, in the method disclosed in Non-Patent Document 1, an edge of a rib cage or a diaphragm is searched, and the edge is fitted using a function of a predetermined order. A method for calculating the accuracy from the standard deviation of the vertical distance has been proposed.

  In addition, as a technique for extracting a target region from a plurality of images that are continuous in space or time, for example, in the medical image diagnostic apparatus disclosed in Patent Document 1, the centroids of the extracted regions correspond to each other at a certain angle. There has been proposed a method of performing correction processing by fitting a curve with an interpolation curve.

Xin-Wei Xu and Kunio Doi, "Image feature analysis and computer-aided diagnosis: Accurate determination of ribcage boundary in chest radiographs", Medical Physics, Volume 22 (5), May 1995, pp.617-626.

JP 2005-224460 A

  However, in Patent Document 1, although described as a method of performing an interpolation process using continuity with respect to the space and the time direction, in the case of a difference in structure on the space axis and a shape change on the time axis, The method of adopting the corresponding interpolation curve is not specified. That is, it is only disclosed that an operator can deal with local shape correction by manually operating an input device such as a mouse. For this reason, since the shape model is fixed or indefinite, the target region cannot be accurately extracted.

  In addition, when the technique of Non-Patent Document 1 is applied to a moving image, since the dose is low for reducing the exposure dose, there is a lot of noise and the image is unclear, and there are problems with the accuracy and stability of extraction. large. In addition, since moving images have a large number of captured images, there is a problem in extraction speed.

  As described above, even if the extraction method of the target region as in Patent Document 1 and Non-Patent Document 1 is performed on a moving image whose target region changes in shape temporally and two-dimensionally, the accuracy is high. The target area cannot be extracted.

  The present invention has been made in view of such circumstances, and provides an image processing technique that realizes stable and high-speed extraction accuracy improvement in extraction of a target area from a moving image that captures the inside of a human body or an animal. The purpose is to provide.

  In order to solve the above-mentioned problem, the invention of claim 1 is a moving image acquisition means for acquiring a moving image in which the physical state of a predetermined region inside a human body or an animal is sequentially captured in the time direction. The frame image is obtained by sequentially obtaining a plurality of frame images included in the moving image and fitting a shape model representing a global shape of the predetermined area to the frame image. Determining a fitting function that approximates the boundary of the predetermined region above, and sequentially obtaining predetermined region boundary information that reflects at least the fitting function; and the shape model used for the fitting process on the frame image. Perform shape model change processing to change sequentially along the time direction A shape model control means, wherein the fitting function is composed of a predetermined number of functions, and the shape model is a model for determining the fitting function, and the predetermined number and an order of each function are allowed. The image processing apparatus includes at least one piece of information in the range.

  The invention according to claim 2 is the image processing apparatus according to claim 1, wherein the shape model further includes information on a fitting application range for each function on the frame image.

  The invention according to claim 3 is the image processing apparatus according to claim 2, wherein the shape model changing process includes: (a) a process of increasing or decreasing the predetermined number; and (b) a function of the predetermined number of functions. At least one of processing for changing the allowable range of the order of at least one function, and (c) processing for changing the fitting application range of at least one function of the predetermined number of functions. Includes processing.

  According to a fourth aspect of the present invention, in the image processing apparatus according to the third aspect, the shape model fitting means is predicted from the image information of the frame image as a boundary of the predetermined region using a predetermined method. And the fitting process includes a process of determining the fitting function based on the boundary candidate information, and the shape model change process is based on the boundary candidate information. At least one of the processes (a) to (c).

  The invention according to claim 5 is the image processing apparatus according to claim 4, wherein in the case where the fitting application range is narrowly changed, the process (a) in the shape model change process is configured to calculate the predetermined number. In the shape model changing process, the process (b) includes a process of reducing the upper limit of the allowable range of the function in the function that narrows the fitting application range among the predetermined number of functions. ,including.

  The invention according to claim 6 is the image processing apparatus according to any one of claims 1 to 5, wherein the shape model change process is a constraint condition in which a predetermined rule or order is stored. A process of changing the shape model based on

  According to a seventh aspect of the present invention, in the image processing apparatus according to any one of the first to fifth aspects of the present invention, when there is a predetermined area adjacent area close to the predetermined area, the frame A time change detecting unit that detects a time change of the predetermined region adjacent to the predetermined region in synchronization with an image and obtains time change information; and the shape model change processing is based on the time change information. Includes processing to change

  The invention according to claim 8 is the image processing apparatus according to claim 1, further comprising fitting result storage means for sequentially storing the predetermined area boundary information output from the shape model fitting means, The shape model changing process includes a first model changing process for changing from a shape model that has been subjected to the fitting process to the frame image to a shape model that is used for a fitting process for a frame image subsequent to the frame image; A second model changing process for changing from a shape model that has been subjected to a fitting process to an image to a shape model that is used for a second fitting process of the frame image, wherein the shape model control means includes the shape model The shape model sequentially changed by the control means or the fitting result storage means Shape model appropriate evaluation means for obtaining evaluation information based on the fitting function in the stored predetermined area boundary information is included, and the shape model control means is configured to perform the first model change process and the first model based on the evaluation information. At least one of the two model change processes is performed.

  The invention according to claim 9 is the image processing apparatus according to claim 8, wherein when the fitting result storage unit stores three or more pieces of the predetermined area boundary information, the evaluation information includes the fitting result. It includes a fitting function group indicating three or more fitting functions in the predetermined area boundary information stored in the storage means.

  The invention according to claim 10 is the image processing apparatus according to claim 8, wherein the shape model control means includes a boundary of the predetermined region that approximates a fitting function in the predetermined region boundary information, and the fitting function. Fitting accuracy calculating means for calculating image comparison information that compares the difference between the boundary of the predetermined area extracted from the image information of the frame image used for deriving the image using a predetermined method and the expected boundary candidate information The evaluation information includes the image comparison information.

  The invention according to claim 11 is the image processing apparatus according to claim 8, wherein the shape model control means sequentially stores the shape models sequentially determined by the shape model control means, thereby fitting history. Fitting history storage means generally stored as information, and the evaluation information includes the fitting history information.

  The invention according to claim 12 is the image processing apparatus according to claim 9, wherein when there is a predetermined region adjacent region close to the predetermined region, the shape model control unit is configured to store the fitting result storage unit. A position shape change calculating means for calculating position shape change information indicating a temporal position displacement amount or shape change of the predetermined region adjacent area between the fitting functions in the predetermined region boundary information stored in The evaluation information includes the position shape change information.

  The invention according to claim 13 is the image processing apparatus according to claim 8, wherein when there are a plurality of predetermined area adjacent areas close to the predetermined area, the shape model control means A positional relationship calculation means for calculating positional relationship information indicating a relative positional relationship between the estimated proximity regions of the predetermined region from which a fitting function in the information is derived, and the evaluation information includes the positional relationship information It is characterized by.

  The invention according to claim 14 is the image processing apparatus according to claim 8, wherein when there is a predetermined area adjacent area close to the predetermined area, the predetermined area adjacent area synchronized with the frame image is displayed. A time change detection means for detecting a periodic time change and obtaining time change information; and the shape model control means responds to a periodic time change of the predetermined region adjacent to the change pattern of the shape model. Priority change pattern storage means for preliminarily storing priority change pattern information created with priority to be applied, wherein the evaluation information is associated with the time change. It includes pattern information.

  The invention according to claim 15 is the image processing apparatus according to any one of claims 1 to 14, wherein the predetermined area includes a lung field area.

  The invention according to claim 16 is the image processing apparatus according to any one of claims 7, 12 to 14, wherein the predetermined area includes a lung field area, and the predetermined area proximity The region includes a diaphragm region, a heart region, and an aorta region.

  According to a seventeenth aspect of the present invention, there is provided a program for causing a computer to function as the image processing apparatus according to any one of the first to sixteenth aspects when executed by a computer included in the image processing apparatus. It is.

  The shape model control means in the image processing apparatus according to any one of claims 1 to 16 executes a shape model change process for sequentially changing a shape model used for a fitting process for a frame image along a time direction. For this reason, it is possible to change the shape model in response to the change in the shape of the predetermined region in the time direction. Thereby, since the predetermined area boundary information can be obtained stably and at high speed, it is possible to improve the extraction accuracy of the predetermined area.

  In addition, since the fitting function is composed of a predetermined number of functions, the fitting function can express the local shape of the predetermined area, and the extraction accuracy of the predetermined area can be improved.

  Furthermore, the shape model is a model for determining a fitting function, and includes at least one information of the predetermined number and an allowable range of the order for each function. Thereby, it is possible to give a constraint condition for obtaining the fitting function. For this reason, a fitting function capable of approximately expressing the boundary of the predetermined region can be determined early and appropriately.

  According to the invention of claim 2, the shape model further includes information on the fitting application range for each function on the frame image. As a result, even when the shape of the predetermined region is complicated, a fitting function capable of more approximately expressing the boundary of the predetermined region can be determined early and appropriately. For this reason, the extraction accuracy of the predetermined region is further improved.

  According to the invention of claim 3, the shape model changing process includes: (a) a process for increasing / decreasing the predetermined number; and (b) changing an allowable range of the order of at least one of the predetermined number of functions. And (c) at least one process of changing the fitting application range of at least one function among the predetermined number of functions. As a result, even when the shape or position of the predetermined region changes significantly in the time direction, it is possible to sufficiently cope with it. For this reason, the extraction accuracy of the predetermined region is further improved.

  According to the invention of claim 4, the fitting process includes a process of determining a fitting function based on the boundary candidate information. As a result, even when the shape of the predetermined region is complicated, a fitting function capable of expressing the boundary of the predetermined region more approximately can be determined earlier and appropriately. For this reason, the extraction accuracy of the predetermined region is further improved.

  Further, the shape model changing process includes (a) a process for increasing / decreasing the predetermined number based on the boundary candidate information, (b) a process for changing the allowable range of the order, and (c) the fitting application range. At least one of the processes to be changed is included. As a result, even when the shape or position of the predetermined region changes significantly in the time direction, the boundary candidate information can be taken into account, so that an error in the fitting process can be prevented. For this reason, it becomes possible to obtain a fitting result more stably and at high speed.

  According to the invention of claim 5, when the fitting application range is changed narrowly, the process (a) in the shape model change process includes a process of reducing the predetermined number, and the process in the shape model change process (B) includes a process of lowering the upper limit of the allowable range of the function in the function that narrows the fitting application range among the predetermined number of functions. Here, since the contents of the processes (a) to (c) are determined according to the boundary candidate information, when the boundary candidate information decreases, the process (a) reduces the number of functions, the process In (b), at least one of processing for lowering the upper limit of the allowable range of the order of the function and processing for narrowing the fitting application range in the processing (c) is performed. Therefore, when the fitting application range is changed narrowly in the process (c), the process of reducing the number of functions in the process (a) and / or the function order allowance in the process (b). It is more preferable to perform a process for reducing the upper limit of the range. As a result, the degree of freedom of the function can be reduced, an error in the fitting process can be prevented, and a stable fitting result can be obtained.

  According to the invention of claim 6, by changing the shape model based on the constraint condition storing a predetermined rule or order, it is possible to place a predetermined constraint on the change of the shape model, and more stably as desired. It is possible to guide the change of the shape model.

  According to the invention of claim 7, by changing the shape model based on the time change information, the shape model can be determined in conformity with the periodic change of the predetermined region adjacent region. For this reason, the extraction accuracy of the predetermined region is further improved.

  9. The shape model control means in the image processing apparatus according to claim 8, wherein the evaluation is obtained based on a shape model sequentially changed by the shape model control means or a fitting function in predetermined area boundary information stored in the fitting result storage means. Based on the information, at least one of the first model change process and the second model change process is executed. As a result, a more accurate shape model can be automatically changed in either the first or second model changing process. In other words, the shape model control means can control the change of the future shape model in time in the first model change process, and the past shape model that has already finished the fitting process in the second model change process. Feedback control becomes possible.

  For this reason, the extraction process of the predetermined area can be performed more stably, and the extraction accuracy is further improved.

  According to the invention of claim 9, the evaluation information includes fitting function groups indicating three or more fitting functions in the predetermined area boundary information stored in the fitting result storage means, whereby the suitability between the fitting functions of the fitting function group is determined. And the second model change process can be performed. Thereby, the fitting accuracy can be improved.

  According to the invention of claim 10, the evaluation information is a predetermined method based on the boundary of the predetermined region approximated by the fitting function in the predetermined region boundary information and the image information of the frame image used for deriving the fitting function. By using the image comparison information that compares the difference between the boundary of the predetermined area extracted and the expected boundary candidate information, the difference from the actual image information obtained by using a predetermined method is referred to Thus, the first or second model change process can be performed, so that fitting accuracy can be improved.

  According to the invention of claim 11, since the evaluation information includes the fitting history information, it is possible to perform the first model change process while referring to the fitting history information, so that the fitting accuracy can be improved. .

  According to the twelfth aspect of the present invention, since the evaluation information includes the position shape change information, if the position shape change is large, it is possible to predict that the fitting accuracy is poor and to execute the second model change process. Therefore, the fitting accuracy can be improved. Conversely, if the change in position and shape is small, it can be predicted that the fitting accuracy is not bad, so that it is possible to determine not to perform the second model change process. Thereby, it becomes possible to prevent a fitting precision deterioration.

  According to the thirteenth aspect of the present invention, the evaluation information includes the positional relationship information, so that when the relative positional relationship between the estimated proximity regions of the predetermined region is different from the assumption, it is predicted that the fitting accuracy is poor and the first Alternatively, since the second model change process can be performed, the fitting accuracy can be improved.

  Conversely, when the relative positional relationship between the estimated proximity regions of the predetermined region is within the assumption, it can be predicted that the fitting accuracy is not bad. For this reason, in the first model change process, a process that does not change the shape model largely, or a process in which the current shape model is used as it is as the future shape model can be performed. In addition, it is possible to determine that the second model change process is not performed. Thereby, it becomes possible to prevent a fitting precision deterioration.

  According to the invention of claim 14, the evaluation information includes the change pattern information with priority associated with the time change, so that the first model change process is performed while referring to the change pattern information with priority. Is possible. As a result, the shape model can be changed in conformity with the periodic change of the predetermined region adjacent region, so that the fitting accuracy can be improved.

  According to the fifteenth aspect of the present invention, since the predetermined area is the lung field area, it is possible to improve the extraction accuracy of the lung field area.

  According to the sixteenth aspect of the present invention, it is possible to improve the extraction accuracy of the lung field region because the predetermined region is the lung field region. In addition, since the predetermined region proximity region is a diaphragm region, a heart region, and an aorta region, a shape model fitting process can be performed in consideration of a region with a relatively large amount of time variation, It becomes possible to fit a more appropriate shape model in the field region.

  According to the invention of claim 17, the same effect as that of the invention of claims 1 to 16 can be obtained.

It is a block diagram which shows the whole structure of the radiation dynamic image imaging system which concerns on each embodiment. It is a block diagram which shows the function structure of the image processing apparatus 3 which concerns on 1st Embodiment. It is a figure which illustrates the dynamic image image | photographed by radiation dynamic image imaging. It is a figure which illustrates a lung field area | region vicinity area | region. It is a figure explaining the characteristic of the fitting function of a lung field area | region. It is a figure explaining a fitting process. It is a flowchart explaining the basic operation | movement of the image processing apparatus 3 implement | achieved in 1st Embodiment. It is a block diagram which shows the function structure of 3 A of image processing apparatuses which concern on 2nd Embodiment. It is a flowchart explaining the basic operation | movement of 3 A of image processing apparatuses implement | achieved in 2nd Embodiment. It is a block diagram which shows the function structure of the image processing apparatus 3B which concerns on 3rd Embodiment. It is a block diagram which shows the function structure of image processing apparatus 3B 'which concerns on 3rd Embodiment. It is a schematic diagram which illustrates a part of waveform measured with the electrocardiograph. It is a schematic diagram which illustrates the fluctuation | variation of the heart wall. It is a schematic diagram which illustrates the fluctuation cycle of the width of the heart. It is a schematic diagram which illustrates the outline extraction of a lung field area | region. It is the schematic diagram which illustrated the position of the feature point of a lung field area | region. It is the schematic diagram which showed the waveform data of respiration information in time series. It is a schematic diagram explaining the change rule of a shape model. It is a flowchart explaining the basic operation | movement of the image processing apparatus 3B implement | achieved in 3rd Embodiment. It is a block diagram which shows the function structure of 3 C of image processing apparatuses which concern on 4th Embodiment. It is a block diagram which shows the function structure of 3 C of image processing apparatuses which concern on 4th Embodiment. It is a schematic diagram explaining whether a fitting function is appropriate. It is a flowchart explaining basic operation | movement of 3 C of image processing apparatuses implement | achieved in 4th Embodiment. It is a flowchart explaining basic operation | movement of 3 C of image processing apparatuses implement | achieved in 4th Embodiment. It is a block diagram which shows the function structure of 3D of image processing apparatuses which concern on 5th Embodiment. It is a schematic diagram explaining performing the appropriate evaluation of a shape model using a fitting function group. It is a flowchart explaining the basic operation | movement of 3D of image processing apparatuses implement | achieved in 5th Embodiment. It is a block diagram which shows the function structure of the image processing apparatus 3E which concerns on 6th Embodiment. It is a schematic diagram explaining performing the appropriate evaluation of a shape model using image comparison information. It is a flowchart explaining the basic operation | movement of the image processing apparatus 3E implement | achieved in 6th Embodiment. It is a flowchart explaining the basic operation | movement of the image processing apparatus 3E implement | achieved in 6th Embodiment. It is a block diagram which shows the function structure of the image processing apparatus 3F which concerns on 7th Embodiment. It is a schematic diagram explaining performing appropriate evaluation of a shape model using fitting history information. It is a flowchart explaining basic operation | movement of the image processing apparatus 3F implement | achieved in 7th Embodiment. It is a block diagram which shows the function structure of the image processing apparatus 3G which concerns on 8th Embodiment. It is a schematic diagram explaining performing the appropriate evaluation of a shape model using position shape change information. It is a flowchart explaining basic operation | movement of the image processing apparatus 3G implement | achieved in 8th Embodiment. It is a block diagram which shows the function structure of the image processing apparatus 3H which concerns on 9th Embodiment. It is a schematic diagram explaining performing the appropriate evaluation of a shape model using positional relationship information. It is a flowchart explaining basic operation | movement of the image processing apparatus 3H implement | achieved in 9th Embodiment. It is a flowchart explaining the basic operation | movement of the image processing apparatus 3H implement | achieved in 9th Embodiment. It is a block diagram which shows the function structure of the image processing apparatus 3I which concerns on 10th Embodiment. It is a schematic diagram explaining performing appropriate evaluation of a shape model using change pattern information with a priority. It is a block diagram which shows the function structure of the image processing apparatus 3I which concerns on 10th Embodiment.

<1. Overall Configuration of Radiation Dynamic Imaging System>
The radiation dynamic image capturing system according to the embodiment of the present invention captures a radiation image of a subject using a human or animal body as a subject, and extracts a target region (predetermined region) from the captured radiation image. In the following embodiments, the target region is described as a lung field region.

  FIG. 1 is a diagram showing an overall configuration of a radiation dynamic image capturing system according to an embodiment of the present invention. As shown in FIG. 1, the dynamic radiographic imaging system 100 (100A, 100B, 100B ′, 100C to 100I) includes an imaging device 1, an imaging control device 2 (imaging console), and an image processing device 3 (3A, 3B, 3B ′, 3C-3I) (diagnostic console) and an electrocardiograph 4. The imaging device 1 and the electrocardiograph 4 and the imaging control device 2 are connected by a communication cable or the like, and the imaging control device 2 and the image processing devices 3 (3A, 3B, 3B ′, 3C-3I) are connected to a LAN (Local It is connected via a communication network NT such as an area network. Each device constituting the dynamic radiographic imaging system 100 (100A, 100B, 100B ′, 100C to 100I) conforms to the DICOM (Digital Image and Communications in Medicine) standard, and communication between the devices conforms to DICOM. Done.

<1-1. Configuration of photographing apparatus 1>
The imaging apparatus 1 is configured by, for example, an X-ray imaging apparatus or the like, and is an apparatus that captures the dynamics of the chest of the subject OB accompanying breathing. Dynamic imaging is performed by acquiring a plurality of images in chronological order while repeatedly irradiating the chest of the subject OB with radiation such as X-rays. A series of images obtained by this continuous shooting is called a dynamic image (moving image). Each of the plurality of images constituting the dynamic image is called a frame image.

  As shown in FIG. 1, the imaging device 1 includes an irradiation unit (radiation source) 11, a radiation irradiation control device 12, an imaging unit (radiation detection unit) 13, a reading control device 14, a cycle detection unit 15, The cycle detection device 16 is provided.

  The irradiation unit 11 irradiates the subject OB with radiation (X-rays) according to the control of the radiation irradiation control device 12. The illustrated example is a system for the human body, and the subject OB corresponds to the person to be inspected. Hereinafter, the subject OB is also referred to as a “subject”.

  The radiation irradiation control device 12 is connected to the imaging control device 2, and performs radiation imaging by controlling the irradiation unit 11 based on the radiation irradiation conditions input from the imaging control device 2.

  The imaging unit 13 is configured by a semiconductor image sensor such as an FPD, and converts the radiation irradiated from the irradiation unit 11 and transmitted through the subject OB into an electrical signal (image information).

  The reading control device 14 is connected to the imaging control device 2. The reading control device 14 controls the switching unit of each pixel of the imaging unit 13 based on the image reading condition input from the imaging control device 2, and switches the reading of the electric signal accumulated in each pixel. Then, the image data is acquired by reading the electrical signal accumulated in the imaging unit 13. Then, the reading control device 14 outputs the acquired image data (frame image) to the imaging control device 2. The image reading conditions are, for example, a frame rate, a frame interval, a pixel size, an image size (matrix size), and the like. The frame rate is the number of frame images acquired per second and matches the pulse rate. The frame interval is the time from the start of the acquisition operation of one frame image to the start of the acquisition operation of the next frame image in continuous shooting, and coincides with the pulse rate.

  Here, the radiation irradiation control device 12 and the reading control device 14 are connected to each other, and exchange synchronization signals to synchronize the radiation irradiation operation and the image reading operation.

  The cycle detection unit 15 detects the breathing cycle of the subject OB and outputs it to the control unit 21 of the imaging control device 2. In addition, the cycle detection unit 15 includes a cycle detection sensor (not shown) that detects the respiratory cycle of the subject OB, and a time measurement unit (not shown) that measures the time of the respiratory cycle detected by the cycle detection sensor and outputs the time to the control unit 21. (Not shown).

<1-2. Configuration of Shooting Control Device 2>
The imaging control device 2 outputs radiation irradiation conditions and image reading conditions to the imaging device 1 to control radiation imaging and radiographic image reading operations by the imaging device 1, and also captures dynamic images acquired by the imaging device 1. Displayed for confirmation of whether the image is suitable for confirmation of positioning or diagnosis.

  As shown in FIG. 1, the photographing control device 2 includes a control unit 21, a storage unit 22, an operation unit 23, a display unit 24, and a communication unit 25, and each unit is connected by a bus 26. ing.

  The control unit 21 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and the like. The CPU of the control unit 21 reads the system program and various processing programs stored in the storage unit 22 in accordance with the operation of the operation unit 23, expands them in the RAM, and performs shooting control processing described later according to the expanded programs. Various processes including the beginning are executed to centrally control the operation of each part of the imaging control device 2 and the operation of the imaging device 1.

  The storage unit 22 is configured by a nonvolatile semiconductor memory, a hard disk, or the like. The storage unit 22 stores various programs executed by the control unit 21 and data such as parameters necessary for execution of processing by the programs or processing results.

  The operation unit 23 includes a keyboard having cursor keys, numeric input keys, various function keys, and the like, and a pointing device such as a mouse. The operation unit 23 is input via a keyboard key operation, a mouse operation, or a touch panel. The indicated instruction signal is output to the control unit 21.

  The display unit 24 is configured by a monitor such as a color LCD (Liquid Crystal Display), and displays an input instruction, data, and the like from the operation unit 23 in accordance with an instruction of a display signal input from the control unit 21.

  The communication unit 25 includes a LAN adapter, a modem, a TA (Terminal Adapter), and the like, and controls data transmission / reception with each device connected to the communication network NT.

<1-3. Configuration of Image Processing Device 3 (3A, 3B, 3B ′, 3C-3I)>
The image processing device 3 (3A, 3B, 3B ′, 3C to 3I) acquires the dynamic image transmitted from the imaging device 1 through the imaging control device 2, and displays an image for a doctor or the like to perform an interpretation diagnosis. To do.

  As shown in FIG. 1, the image processing apparatus 3 (3A, 3B, 3B ′, 3C-3I) includes a control unit 31 (3A, 3B, 3B ′, 3C-3I), a storage unit 32, and an operation unit 33. A display unit 34 and a communication unit 35, and each unit is connected by a bus 36.

  The control unit 31 (31A, 31B, 31B ', 31C to 31I) includes a CPU, a RAM, and the like. The CPU of the control unit 31 (31A, 31B, 31B ′, 31C to 31I) reads the system program and various processing programs stored in the storage unit 32 in accordance with the operation of the operation unit 33 and develops them in the RAM. Then, various processes are executed according to the developed program, and the operation of each part of the image processing apparatus 3 (3A, 3B, 3B ′, 3C-3I) is centrally controlled (details will be described later).

  The storage unit 32 is configured by a nonvolatile semiconductor memory, a hard disk, or the like. The storage unit 32 stores various programs executed by the control unit 31, parameters necessary for execution of processing by the programs, or data such as processing results. For example, the storage unit 32 stores an image generation processing program for executing an image generation process described later. These various programs are stored in the form of readable program codes, and the control unit 31 (31A, 31B, 31B ', 31C to 31I) sequentially executes operations according to the program codes.

  The operation unit 33 includes a keyboard having cursor keys, numeric input keys, various function keys, and the like, and a pointing device such as a mouse. The operation unit 33 is input via a keyboard key operation, a mouse operation, or a touch panel. The instruction signal is output to the control unit 31 (31A, 31B, 31B ′, 31C to 31I).

  The display unit 34 is configured by a monitor such as a color LCD, and in accordance with an instruction of a display signal input from the control unit 31 (31A, 31B, 31B ′, 31C to 31I), an input instruction from the operation unit 33, data, and A display image to be described later is displayed.

  The communication unit 35 includes a LAN adapter, a modem, a TA, and the like, and controls data transmission / reception with each device connected to the communication network NT.

<1-4. Configuration of electrocardiograph 4>
In FIG. 1, the electrocardiograph 4 is shown apart from the subject OB, but in reality, each electrode terminal of the electrocardiograph 4 is attached to the subject OB, and the heart of the subject OB is shown. The radio wave form is output as a digital signal.

  As shown in FIG. 1, the electrocardiograph 4 includes a phase detection unit 41, and the phase detection unit 41 performs an imaging operation by the imaging device 1 in response to a control signal from the CPU of the control unit 21. As basic information for synchronization, the heartbeat phase of the subject OB is detected.

<2. First Embodiment>
The image processing device 3 of the dynamic radiographic image capturing system 100 according to the first embodiment of the present invention sequentially performs the extraction process of the lung field region of the subject OB on the captured moving image in the time direction.

  Below, the functional structure implement | achieved by the image processing apparatus 3 is demonstrated.

<2-1. Functional configuration of image processing apparatus 3>
FIG. 2 is a diagram showing a functional configuration realized by the control unit 31 together with other configurations in the image processing apparatus 3 in the radiation dynamic image capturing system 100 when a CPU or the like operates according to various programs. The image processing apparatus 3 of this embodiment uses a dynamic image in which a chest including mainly the heart and both lungs is photographed.

  The control unit 31 mainly includes a moving image acquisition unit 110, a shape model fitting unit 120, a shape model control unit 130, and a target area extraction unit 140.

  In the following description, the functional configuration of the control unit 31 as shown in FIG. 2 is described as being realized by executing a program installed in advance, but may be realized with a dedicated hardware configuration.

  Hereinafter, specific contents of each process performed by the moving image acquisition unit 110, the shape model fitting unit 120, the shape model control unit 130, and the target region extraction unit 140 will be sequentially described with reference to FIG.

<2-1-1. Moving Image Acquisition Unit 110>
In the moving image acquisition unit 110, a moving image in which the physical state of a lung field region (predetermined region) inside the human body or animal photographed by the reading control device 14 of the imaging device 1 is sequentially captured in the time direction. Get a statue. The term “physical state” as used herein refers to the geometric shape of the lung field region and also includes the concentration of blood flow (the presence or absence of blood flow).

  In FIG. 2, the imaging control device 2 is interposed between the imaging device 1 and the image processing device 3, and the detection data stored in the storage unit 22 of the imaging control device 2 is transmitted via the communication unit 25. The data is output to the communication unit 35 of the processing device 3.

  FIG. 3 is a diagram exemplifying a moving image captured by radiodynamic image capturing with respect to the dynamics of the chest of the subject OB accompanying breathing. As shown in FIG. 3, the frame images G1 to G10 acquired by the moving image acquisition unit 110 are obtained by continuously capturing one cycle of the respiratory cycle at a fixed capturing timing. Specifically, the images taken at the shooting timings at times t = t1, t2, t3,..., T10 correspond to the frame images G1, G2, G3,.

  However, since the moving image acquired in this way has a low dose compared to a still image, the image is a noisy image with a lot of noise, and there are problems with the extraction accuracy and stability of the lung field region. In addition, since a moving image in which the shape of the lung field changes in time and space has a large number of shots, an improvement in the extraction speed of the lung field is desired.

  Therefore, in the present invention, the following processing for changing the shape model expressing the boundary of the lung field region in the time direction is performed on the frame image G included in the moving image, so that the extraction accuracy can be stably and rapidly increased. Make improvements happen.

<2-1-2. Shape Model Fitting Unit 120>
The shape model fitting unit 120 sequentially acquires a plurality of frame images G included in the moving image acquired by the moving image acquisition unit 110, and expresses the global shape of the lung field region with respect to the frame image G. By performing a fitting process for fitting the shape model, a fitting function that approximates the boundary of the lung field region on the frame image G is determined, and lung field region boundary information (predetermined region boundary information) that reflects at least the fitting function ) In order.

  The “fitting function” here is composed of a predetermined number of functions, and the “shape model” is a model for determining the fitting function, and (A) the predetermined number and (B) the order of each function. It includes at least one piece of information within the allowable range and (C) the fitting application range for each function on the frame image G.

  In the fitting process according to the present embodiment, a frame image with a boundary line, which will be described later, is sequentially output, and lung field region boundary information is described as a “frame image with a boundary line”.

  In addition, the lung field region boundary information includes a lung field region and a lung field region neighboring region in the case where there is a lung field region neighboring region (predetermined region neighboring region) such as a diaphragm region and a heart region adjacent to the lung field region. It is preferable that the boundary portion of is considered. In the present embodiment, the diaphragm region, the heart region, and the aorta region are assumed as the lung region adjacent region.

  FIG. 4 is a diagram illustrating a lung field region proximity region, FIG. 4 (a) is a diagram illustrating a boundary P1 of a heart region corresponding to the lung field region proximity region, and FIG. 4 (b) is a lung field region. It is a figure which shows the boundary P2 of the aorta area | region equivalent to a proximity | contact area | region, and the boundaries P3 and P4 of a diaphragm area | region. As shown in FIG. 4, in determining the shape model of the lung field region, the boundary part of the lung field region adjacent region such as the heart region boundary P1, the aorta region boundary P2, the diaphragm region boundaries P3, P4, etc. The fitting function is calculated in consideration.

  FIG. 5 is a diagram for explaining the characteristics of the fitting function F in the lung field region. That is, the fitting function F in FIG. 5 shows an example in which 13 functions f are formed as the predetermined number of functions f. As shown in FIG. 5, even if the functions f1 to f13 are used even in a geometric shape such as a lung field region, it can be expressed more approximately.

  As described above, the fitting function F may be composed of a plurality of curves such as a spline curve as well as a curve and a straight line, and is expressed by the order of each function f.

  Further, the shape model M (see FIG. 2) in which the fitting function F in the example of FIG. 5 is determined is the allowable range of the order for each of the functions f of 13 in (A) and 13 in (B), and (C). Then, it is at least one piece of information in the fitting application range for each of the 13 functions f on the frame image G.

  The outline of the shape model M will be described using one of the 13 functions f in (A) as an example. For example, in the shape model M in the region of the boundary P3 in FIG. 4, the above (A) is one function f, the above (B) is the 1 function f is a quadratic function, and the fitting of (C) above. The scope of application has information of region R3. Then, the fitting function F is determined as the function f1 (see FIG. 5) by performing the fitting process on the boundary P3 region using the shape model M.

  Similarly, in the shape model M in the region of the boundary P4 in FIG. 4, (A) is one function f, and (B) is the first function f is a quadratic to cubic function. The fitting application range of (C) has information that is the region R4. Then, the fitting function F is determined as the function f7 (see FIG. 5) by performing the fitting process on the boundary P4 using the shape model M.

  In this way, the same information as the boundaries P3 and P4 exists in all the boundary regions to be extracted, and the shape model M is configured.

  Specifically, each function f can be expressed by a polynomial function in the X, Y coordinate system, for example. The polynomial function is the equation Y = aX ^ n + bX ^ (n-1) + ... with X as a variable, or X = aY ^ n + bY ^ (n-1) + ... with Y as a variable. , And it is possible to fit the frame image G by finding the optimum values of the coefficients a, b,. Further, X and Y may be an elliptical function by setting them as simultaneous variables.

  Each function f is, for example, a function having X or Y as a variable, the value of the maximum degree of this polynomial (corresponding to n in the above formula), and the corresponding function in which range on coordinates It is expressed by information such as whether it appears in

  In addition, in order to actually calculate each function f, (x1, y1) is obtained by searching for a point that becomes a boundary of the lung field region with respect to binary region information and an edge to be described later until X = x1 to xn. ), (x2, y2)... (xn, yn) is obtained, and a function can be calculated by finding the coefficient values a, b,. The closest approximation can be determined by using, for example, the least square method.

  As described above, the shape model fitting unit 120 performs the fitting process of the shape model M, and uses the frame image GF with a boundary line (see FIG. 5) with the determined fitting function (that is, the boundary line) F attached to the frame image G. Get sequentially. That is, the determined fitting function (boundary line) F is a function fitted using, for example, the least square method as described above.

  Next, processing performed by the shape model fitting unit 120 will be described. That is, the shape model fitting unit 120 further performs a process of extracting boundary candidate information that is predicted to be a boundary of the lung field region from the image information of the frame image G using a predetermined method. In the fitting process, a process for determining the fitting function F is performed based on the boundary candidate information.

  FIG. 6 is a diagram for explaining a fitting process method by the shape model fitting unit 120. FIG. 6A shows a fitting process for image information (0/1 area information) whether or not a lung field area. FIG. 6B shows a direct fitting process for image density information.

  As shown in FIG. 6A, an area IR ("1" area) indicated by dots classified by binary information and other area ("0" area) information (boundary candidate information) are included. On the other hand, when the fitting process of the shape model M is performed (the fitting function F is obtained), the shape model M is obtained by the effect of correcting the extraction region and the smoothness. This is because the unevenness portion IRb of the region IR different from the original shape model M is considered as noise, and the noise portion is smoothed to obtain the fitting function F. Therefore, it is possible to improve accuracy and stabilize the lung field region. A process to compensate for an extraction error can be performed. As a region extraction method for extracting 0/1 region information from an image, a region growing method or a graph cut (for example, “Fast Apporoximate Energy Minimization via Graph Cuts”, Y. Boykov, O Veksler, R. Zabih. IEEE Transactions on Pattern Analysis and Machine Intelligence 23: 1222-1239: 2001 etc.) can be used.

  On the other hand, there is also a method of performing a fitting process of the shape model M directly from multi-value image density information (boundary candidate information). As illustrated in FIG. 6B, the function f7 is subjected to fitting processing while performing edge determination in order to extract the contour of the lung field region from the image density. To extract the contour of the lung field from the image, as an extraction method using the edge as information, Snakes (for example, "Snakes: Active contour models.", M. Kass, A. Witkin, and D. Terzopoulos , International Journal of Computer Vision, Vol.1, No.4, pp.321-331, 1988.) and the like, it is possible to use a method using an active contour model.

  As described above, the fitting process of the shape model M can be performed in consideration of regions having a relatively large amount of time variation such as the diaphragm region, the heart region, and the aorta region as the lung field region adjacent region, It becomes possible to fit a more appropriate shape model M in the lung field region.

<2-1-3. Shape Model Control Unit 130>
Subsequently, the shape model control unit 130 performs shape model change processing for sequentially changing the shape model M used for the fitting processing for the frame image G along the time direction, and performs the fitting processing using the changed shape model M. A command is given to the shape model fitting unit 120 to be executed.

  The “shape model changing process” referred to herein includes (a) a process for increasing / decreasing a predetermined number of functions, and (b) a process for changing the allowable range of the order of at least one function among the predetermined number of functions. (C) at least one process of changing a fitting application range of at least one function among the predetermined number of functions. Moreover, it is preferable that the processes (a) to (c) are performed based on the boundary candidate information.

  For example, the shape model changing process will be described in the case where the shape model M of the next frame image G is changed based on the determined fitting function F in FIG. 5. In (a), 13 functions f1 to f13 are increased or decreased. It becomes possible. Of the thirteen functions f1 to f13, the order of at least one function f can be changed in (b), and the fitting application range of at least one function f can be changed in (c).

  It is desirable that the shape model control unit 130 not only greatly changes the shape model M in terms of time but also draws smoothly with little movement of the fitting function F itself. However, in the case of imaging with insufficient fps (frame per second) due to heart movement caused by heartbeat, since there is a sudden function change between the two frame images G, smoothing processing according to the region is performed. Necessary. In the chest X-ray moving image, in particular, three movements of body movement, breathing, and heartbeat are assumed. It is desirable to hold in advance the upper limit value of the abrupt movement caused by three causes for each area and allow only the movement within the assumption. For example, an unexpected motion can be excluded as an outlier and the interpolation value can be obtained from the frame images G before and after the target frame image G. In addition, since breathing and heartbeats periodically move in a certain pattern, it is desirable to allow only periodic movements.

<2-1-4. Target Area Extraction Unit 140>
The target area extraction unit 140 sequentially acquires the frame image with boundary line GF subjected to the fitting process from the shape model fitting unit 120 and stores it in the storage unit 32.

<2-2. Basic Operation of Image Processing Device 3>
FIG. 7 is a flowchart for explaining a basic operation realized in the image processing apparatus 3 according to the first embodiment. Since the individual functions of each unit have already been described (see FIG. 2), only the overall flow will be described here.

  As shown in FIG. 7, first, in step S <b> 11, the moving image acquisition unit 110 of the control unit 31 acquires a moving image captured by the reading control device 14 of the imaging device 1 via the imaging control device 2.

  In step S12, the shape model fitting unit 120 applies boundary candidate information (0/1 region information (see FIG. 6A)) or image density information (see FIG. 6A) to the frame image G in the moving image acquired in step S11. 6 (b))), the shape model M is fitted to obtain a frame image GF with a boundary line (see FIG. 5), and the frame image GF with a boundary line is extracted from the target region extraction unit. Output to 140.

  In step S13, the control unit 31 determines whether or not fitting processing has been performed on all the frame images G. If there is unprocessed, the process proceeds to step S14. If there is no unprocessed, the operation flow is as follows. Is terminated.

  In step S <b> 14, the shape model control unit 130 changes the shape model M used for obtaining the frame image GF with a boundary line, and causes the shape model fitting unit 120 to fit the changed shape model M to the next frame image G. A command is given and the process proceeds to step S12.

  In step S12, the shape model fitting unit 120 newly inputs using the shape model M changed in step S14 and performs a fitting process on the frame image G.

  As described above, the steps S12 to S14 are repeated until the fitting process is completed for all the frame images G. After the fitting process is completed for all the frame images G, the operation flow is completed. .

  As described above, in the image processing device 3 according to the first embodiment, the shape model control unit 130 performs the shape model change process for sequentially changing the shape model M used for the fitting process for the frame image G along the time direction. . For this reason, it becomes possible to change the shape model M in response to the change in the shape of the lung field region in the time direction. As a result, it is possible to obtain a frame image GF with boundary line (lung field region boundary information) stably and at high speed, and thus it is possible to improve the extraction accuracy of the lung field region.

  In addition, since the fitting function F is composed of a predetermined number of functions f, the fitting function F can express the local shape of the lung field region, and the extraction accuracy of the lung field region can be improved. (FIG. 5).

  The shape model M is a model for determining the fitting function F, and includes at least one piece of information within the predetermined number and the allowable range of the order for each function f. Thereby, it is possible to give a constraint condition for obtaining the fitting function F. For this reason, the fitting function F that can approximately represent the boundary of the lung field region can be determined early and appropriately.

  The shape model M further includes information on the fitting application range for each function f on the frame image G. As a result, even when the shape of the lung field region is complicated, the fitting function F capable of more approximately expressing the boundary of the lung field region can be determined early and appropriately. For this reason, the extraction accuracy of the lung field region is further improved.

  Further, the shape model changing process includes (a) a process for increasing / decreasing a predetermined number of functions f, (b) a process for changing an allowable range of the order of at least one function f among the predetermined number of functions f, and ( c) At least one process is included among the processes of changing the fitting application range of at least one function f among the predetermined number of functions f. As a result, even when the shape or position of the lung field region changes remarkably in the time direction, it is possible to sufficiently cope with it. For this reason, the extraction accuracy of the predetermined region is further improved.

  The fitting process includes a process for determining the fitting function F based on the boundary candidate information. As a result, even when the shape of the lung field region is complicated, the fitting function F capable of expressing the boundary of the lung field region more approximately can be determined earlier and appropriately. For this reason, the extraction accuracy of the lung field region is further improved.

  Furthermore, the shape model change process includes at least one of the above (a) to (c) based on the boundary candidate information. As a result, even when the shape or position of the lung field region changes significantly in the time direction, the boundary candidate information can be taken into account, so that an error in the fitting process can be prevented. For this reason, it becomes possible to obtain a fitting result more stably and at high speed.

<3. Second Embodiment>
FIG. 8 is a diagram illustrating a functional configuration of the control unit 31A used in the image processing apparatus 3A (see FIG. 1) configured as the second embodiment of the present invention. The control unit 31A is used as an alternative to the control unit 31 (see FIG. 2) in the image processing apparatus 3 of the first embodiment. The difference from the first embodiment is that a shape model control unit 130A corresponding to the shape model control unit 130 of the first embodiment includes a constraint condition storage unit 135A. The remaining configuration is the same as that of the image processing apparatus 3.

<3-1. Constraint Condition Storage Unit 135A>
The constraint condition storage unit 135A stores constraint conditions such as predetermined rules and order used for the shape model change process. Then, the shape model control unit 130A performs a shape model change process based on the constraint condition.

  That is, since the probability that the shape model M is significantly changed in the time direction is low, in determining the shape model M of the next frame image G, a constraint condition for maintaining the shape model M in the previous frame image G is provided, By storing the constraint condition that limits the increase / decrease in the number of f to “± 1” in advance and the constraint condition that limits the increase / decrease in the order of each function f to “± 1” in advance, the stable shape model M Changes can be made.

<3-2. Basic Operation of Image Processing Device 3A>
Subsequently, FIG. 9 is a diagram illustrating an operation flow of the image processing apparatus 3A according to the second embodiment. In FIG. 9, steps S21 to S23 are the same as steps S11 to S13 of FIG.

  In the second embodiment, only the following steps are changed by adding the constraint condition storage unit 135A that did not exist in the first embodiment.

  That is, as a process similar to that of the first embodiment, after passing through steps S21 to S23, as shown in FIG. 9, the shape model control unit 130A is attached to the frame image with boundary line GF in step S24. The shape model fitting unit 120 changes the boundary line (fitting function F) based on a predetermined rule or order stored in the constraint condition storage unit 135A and causes the changed shape model M to be fitted to the next frame image G. A command is given and the process proceeds to step S22. The remaining steps are the same as in the first embodiment.

  As described above, in the image processing device 3A according to the second embodiment, the shape model M is changed on the basis of the constraint condition that stores a predetermined rule or order, thereby applying predetermined constraints on the change of the shape model M. Therefore, it is possible to guide the change of the desired shape model M more stably.

<4. Third Embodiment>
10 and 11 are diagrams showing a functional configuration of the control units 31B and 31B ′ used in the image processing apparatuses 3B and 3B ′ (see FIG. 1) configured as the third embodiment of the present invention. The control units 31B and 31B ′ are used as an alternative to the control unit 31 (see FIG. 2) in the image processing apparatus 3 of the first embodiment. The difference from the first embodiment is that time change detection units 115B and 115B ′ are further provided, and the shape model control units 130B and 130B ′ receive the output results of the time change detection units 115B and 115B ′. The remaining configuration is the same as that of the image processing apparatus 3.

<4-1. Time change detection unit 115B, 115B ′>
The inter-period change detection units 115B and 115B ′ acquire time change information T by detecting a periodic time change in the lung field region synchronized with the frame image G (see FIGS. 10 and 11). The time change information T is a periodic time change in a region having a relatively large amount of time fluctuation in the lung field region. Here, the respiratory cycle change and the heartbeat cycle change will be described as examples.

  Below, the calculation method about a cardiac cycle change and a respiratory cycle change is demonstrated.

<4-1-1. First Heart Rate Information Detection Method: Electrocardiograph Detection Result>
As a first heartbeat information detection method, the time change detection unit 115B uses the result obtained from the phase detection unit 41 of the electrocardiograph 4 as shown in FIG. That is, the time change detection unit 115B is configured to acquire a heartbeat cycle change from the outside. FIG. 12 is a diagram illustrating one cycle of the electrocardiogram waveform of the subject OB. In FIG. 12, the horizontal axis indicates the time, and the vertical axis indicates the magnitude (voltage) of the electric signal, and the so-called P wave, Q wave, R wave, S wave, T wave, and U wave shapes are shown. Curves showing changes in electrical signals including curves Pp, Qp, Rp, Sp, Tp and Up are shown.

  Therefore, the time change detection unit 115B acquires the heart cycle change by detecting the above points (Pp, Qp, Rp, Sp, Tp and Up) based on the detection result acquired from the phase detection unit 41. To do.

  The detection operation by the phase detector 41 is performed in synchronization with the imaging operation by the imaging device 1 (see FIG. 1).

  As described above, the time change detection unit 115B can automatically acquire a periodic time change of the heart by acquiring a heartbeat period change from the outside.

<4-1-2. Second Heart Rate Information Detection Method: Heart Wall Movement Amount>
On the other hand, as a second heart rate information detection method, as shown in FIG. 11, the time change detection unit 115B ′ calculates the amount of motion of the heart wall using the captured image acquired by the moving image acquisition unit 110. Then, it is set as heart rate information. That is, the time change detection unit 115B ′ detects the heartbeat cycle based on the fluctuation of the heart wall captured in the moving image, and then outputs it to the shape model control unit 130B ′ (see FIG. 11). Specifically, the phase of the pulsation of the heart at the timing when each frame image is captured is detected by detecting the fluctuation of the heart wall from the moving image. Therefore, the heart wall is detected as the phase of the heart beat.

  FIG. 13 is a schematic diagram illustrating the fluctuation of the heart wall captured in the moving image. As shown in FIG. 13, as an example of the fluctuation of the heart wall HL, the fluctuation of the lateral width of the heart is adopted. FIGS. 13A to 13C illustrate a state in which the lateral width of the heart increases from w1 to w3 in the process of expanding the heart.

  Therefore, the time change detection unit 115B 'detects the heartbeat cycle by detecting the lateral width of the heart from each frame image. Specifically, as a method for detecting the lateral width of the heart, for example, a method for detecting the outline of the heart can be cited. As a method for detecting the outline of the heart, various known methods can be employed. For example, using a model (heart model) indicating the shape of the heart, a feature point in the X-ray image, Techniques for detecting the outline of the heart by matching with the feature points of the heart model (for example, "Image feature analysis and computer-aided diagnosis in digital radiography: Automated analysis of sizes of heart and lung in chest images", Nobuyuki Nakamori et al., Medical Physics, Volume 17, Issue 3, May, 1990, pp.342-350.

  FIG. 14 is a schematic view exemplifying the relationship between the time when the image was taken and the lateral width of the heart for a plurality of frame images constituting the moving image. In FIG. 14, the horizontal axis indicates time, the vertical axis indicates the width of the heart, and the value of the width of the heart where a circle is detected is illustrated.

  Here, if the lateral width of the heart captured at time t is Hwt, the lateral width of the heart captured at time t + 1 is Hwt + 1, and if (Hwt + 1−Hwt) ≧ 0 holds, it is captured at time t. The obtained frame image is classified when the heart is expanded, and if (Hwt + 1−Hwt) <0 holds, the frame image captured at time t is classified when the heart contracts.

  In this way, by detecting the lateral width of the heart, that is, the fluctuation of the heart wall HL, it is possible to classify the time of dilation and contraction of the heart, so that the phase of the heart beat can be detected.

  As described above, the time change detection unit 115 </ b> B ′ can automatically acquire the heartbeat period by detecting the heartbeat period based on the motion of the heart wall captured in the moving image.

  In addition, since the 2nd heart rate information detection method detects a heart rate cycle indirectly compared with the 1st heart rate information detection method, it is estimated that a noise component is easy to be included. Therefore, it is preferable that the time change detection unit 115 </ b> B ′ detects the heartbeat period using frequency analysis or the like based on the motion of the heart wall HL captured in the moving image.

<4-1-3. First Respiratory Information Detection Method: Measurement Result with Another Device>
As a first respiratory information detection method, a measurement result by another device is used (see FIG. 10). That is, the time change detection unit 115B is configured to be able to set the respiratory cycle from the outside. As a method of measuring with another device, for example, an apparatus as described in Japanese Patent No. 3793102 can be used. Also, a method implemented by monitoring with a sensor composed of a laser beam and a CCD camera (for example, "Study on sleep monitoring of sleepers using FG visual sensor", Hiroto Aoki, Masato Nakajima, Society of Electronics, Information and Communication Engineers Society Conference Lectures 2001. Information and Systems Society Conference Proceedings, 320-321, 2001-08-29, etc.) can also be adopted.

  In the present embodiment, the cycle detector 15 of the cycle detector 16 can be used like the time change detector 115B shown in FIG.

  As described above, the time change detection unit 115B can automatically acquire a periodic time change of the diaphragm by acquiring a respiratory cycle from the outside.

<4-1-4. Second respiratory information detection method: area value or distance between feature points>
On the other hand, as a second respiration information detection method, as shown in FIG. 11, the time change detection unit 115B ′ uses the captured image acquired by the moving image acquisition unit 110 to use the area value or feature point of the lung field. Respiratory information is obtained by calculating the inter-distance. That is, the time change detection unit 115B ′ detects the respiratory cycle based on the change in the area value of the lung field region or the distance between feature points captured in the moving image, and then outputs it to the shape model control unit 130B ′ (FIG. 11). reference). The lung field area can be obtained by extracting the contour of the lung field and defining the number of pixels in the region surrounded by the contour as the lung field region.

  FIG. 15 is a schematic diagram illustrating the contour extraction of the lung field. As shown in FIG. 15, the lung field may be extracted for each of the left and right sides, or may be extracted as a contour including the heart and spine regions. Extraction methods include conventional techniques (eg, “Image feature analysis and computer-aided diagnosis: Accurate determination of ribcage boundary in chest radiographs”, Xin-Wei Xu and Kunio Doi, Medical Physics, Volume 22 (5), May 1995, pp.617-626 etc.) can be employed.

  As described above, the time change detection unit 115B ′ performs the extraction of the contour OL of the lung field using the acquired captured image, and uses the number of pixels in the extracted region as the feature amount. Detect as the area of the lung field.

  As a second respiration information detection method, it is also possible to calculate the distance between feature points of the lung field region using the captured image and use it as respiration information. That is, lung field extraction is performed in the same manner as described above, and two feature points are obtained from the extracted region, and a feature amount is calculated by obtaining a distance between the two points.

  FIG. 16 is a diagram illustrating positions of feature points in the lung field region. When calculating the change in the length (lung field length) from the upper end LT to the lower end LB of the lung region, in FIG. 16 (a), the lung apex is the upper end LT of the lung region, and from the lung apex to the body axis direction. FIG. 16B shows an example in which the intersection of the lowered straight line and the diaphragm is extracted as the lower end LB of the lung region. In FIG. 16B, the lung apex is extracted as the upper end LT of the lung region, and the lateral angle is extracted as the lower end LB of the lung region. This is an example.

  FIG. 17 is a schematic diagram showing waveform data of respiratory information detected by the time change detection unit 115B ′ in time series, and calculates feature amounts such as area values of lung field regions or distances between feature points. As a result of monitoring. As shown in FIG. 17, one period of the respiration period (respiration cycle) B is composed of inspiration and expiration, and consists of one expiration and one inspiration. In inhalation, the area of the lung field in the thorax increases as the diaphragm lowers and breathes in. The maximum inhalation time B1 is when the maximum amount of breath is inhaled (the conversion point between inspiration and expiration). In expiration, the region of the lung field becomes smaller as the diaphragm rises and exhales, but when exhaling to the maximum (conversion point between exhalation and inspiration) becomes the maximum exhalation time B2.

  As described above, the temporal change detection unit 115B ′ automatically detects the respiratory cycle based on the temporal change in the area value of the lung field region or the distance between the feature points captured in the moving image. It can be acquired.

  Moreover, since the 2nd respiratory information detection method detects a respiratory cycle indirectly compared with the 1st respiratory information detection method, it is estimated that a noise component is easy to be included. Therefore, it is preferable that the time change detection unit 115B ′ detects the respiratory cycle using frequency analysis or the like based on the temporal change of the area value of the lung field region or the distance between feature points captured by the moving image.

<4-2. Shape Model Control Units 130B, 130B ′>
Since the control units 31B and 31B ′ include the time change detection units 115B and 115B ′, the shape model control units 130B and 130B ′ are based on the respiratory cycle changes and heartbeat cycle changes output from the time change detection units 115B and 115B ′. To change the shape model.

  Below, the example which performs a shape model change process based on a respiratory cycle change is demonstrated. FIG. 18 is a schematic diagram for explaining a change rule of the function f (shape model M) based on a respiratory cycle change. 18 (a) to 18 (c) are diagrams showing that the appearance of the image of the frame image G is different corresponding to the respiratory cycle change, and in FIG. 18 (d), the image of the frame image G is shown. It is a figure explaining the setting rule of the order of the function f which considered that the appearance of is different. Note that the order of the function f in each frame image G is determined after performing edge detection on the image.

  A frame image Ga shown in FIG. 18A corresponds to exhalation in the change of the respiratory cycle, and the boundary P4 of the diaphragm region corresponding to the lung field region proximate region tends to be hidden on the image. Therefore, the setting of the function f71 is defined as no function (see FIG. 18D).

  A frame image Gb shown in FIG. 18 (b) corresponds to an intermediate position between exhalation and inspiration in the change of the respiratory cycle, and the boundary P4 of the diaphragm region corresponding to the lung region adjacent region is linear or It tends to appear as a curve. Therefore, the function f72 is defined as a linear function or a quadratic function (see FIG. 18D).

  A frame image Gc shown in FIG. 18C corresponds to inspiration in the change of the respiratory cycle, and the boundary P4 of the diaphragm region corresponding to the lung region adjacent region tends to appear as an S-shaped curve on the image. . Therefore, the function f73 is defined as a cubic function (see FIG. 18D).

  As described above, since the diaphragm changes in order with inspiration and decreases in order with respect to the breathing phase, the change destination of the shape model M may be defined in advance according to the phase. preferable. In other words, in the inspiratory phase, the lungs expand and approach the state where the shape of the heel lateral angle can be seen well, so a rule is set so that the order of the diaphragm function increases. Conversely, in the expiration phase, since the lungs contract and hide in other organs, a rule is set so that the order is reduced (see FIG. 18D). By making the rules in this way, if it is known whether the frame image G is located in expiration or inspiration, the change process of the shape model M can be changed, so that the shape model change process based on the respiratory cycle change Can be performed.

  In addition, not only respiratory cycle changes such as diaphragm hiding, but also changes in heart shape due to heartbeat phase (see FIGS. 13 and 14) and changes due to aortic occlusion, etc., preliminarily limited rules are formed, and shapes according to the rules are determined. Model change processing can be performed.

  Further, when the fitting application range in the process (c) is narrowly changed, the shape model control means 130B, 130B ′ includes a process of reducing a predetermined number of functions f as the process (a) in the shape model change process; As the process (b), among the predetermined number of functions f, the function f for narrowing the fitting application range is performed to lower the upper limit of the allowable range of the order of the function f.

  That is, since the processing contents (a) to (c) are determined according to the boundary candidate information, when the boundary candidate information decreases, the processing (a) reduces the number of functions, and the processing ( In b), at least one of processing for lowering the upper limit of the allowable range of the order of the function and processing for narrowing the fitting application range in the processing (c) is performed. Accordingly, when the fitting application range is narrowly changed in the process (c), the function order is reduced in the process (a) and / or the function order is allowed in the process (b). It is more preferable to perform a process of lowering the upper limit of the range.

  Here, in the example of FIG. 18, in the shape model change process when moving from the frame image Gc (see FIG. 18C) to the frame image Gb (see FIG. 18B), the above process (b) is performed. In the function f73 for narrowing the fitting application range, processing for lowering the upper limit of the allowable range of the order of the function f73 (that is, changing from a cubic function to a quadratic or linear function) is performed. Further, in the shape model change process when moving from the frame image Gb (see FIG. 18B) to the frame image Ga (see FIG. 18A), the function f72 is changed from the function f72 to no function as the process (a) (that is, , The number of functions is changed to −1).

  Thus, when the fitting application range is narrowly changed in the process (c), the order of the function is reduced in the process (a) and / or the process (b) in which the number of functions is reduced. By performing the process of lowering the upper limit of the allowable range, it is possible to reduce the degree of freedom of the function, prevent an error in the fitting process, and obtain a stable fitting result.

<4-3. Basic Operation of Image Processing Devices 3B and 3B ′>
Subsequently, FIG. 19 is a diagram exemplarily showing an operation flow of the image processing apparatus 3B among the image processing apparatuses 3B and 3B ′ according to the third embodiment. In FIG. 19, steps S32 to S33 are the same as steps S12 to S13 of FIG.

  In the third embodiment, the following process is changed by adding the time change detection unit 115B that did not exist in the first embodiment.

  As shown in FIG. 19, first, in step S <b> 31, the moving image acquisition unit 110 of the control unit 31 </ b> B acquires a moving image captured by the reading control device 14 of the imaging device 1 via the imaging control device 2. The time change detection unit 115 </ b> B acquires the respiratory cycle change from the cycle detection unit 15 of the imaging device 1, and acquires the heartbeat cycle change from the phase detection unit 41 of the electrocardiograph 4.

  Then, as steps similar to those in the first embodiment, through step S32 to step S33, in step S34, the shape model control unit 130B uses the boundary line (fitting function F) attached to the frame image with boundary line GF. A change is made based on the time change information T detected by the time change detection unit 115B, a command is given to the shape model fitting unit 120 to fit the changed shape model M to the next frame image G, and the process proceeds to step S32. The remaining steps are the same as in the first embodiment.

  As described above, in the image processing device 3B (3B ′), the shape model M can be determined in conformity with the periodic change of the lung field adjacent region by changing the shape model M based on the time change information. . For this reason, the extraction accuracy of the lung field region is further improved.

<5. Fourth Embodiment>
20 and 21 are diagrams showing a functional configuration of the control unit 31C used in the image processing apparatus 3C (see FIG. 1) configured as the fourth embodiment of the present invention. This control unit 31C is used as an alternative to the control unit 31 (see FIG. 2) in the image processing apparatus 3 of the first embodiment. The difference from the first embodiment is that a fitting result storage unit 125 is provided, and that the shape model control unit 130C includes a shape model appropriate evaluation unit 135C. The remaining configuration is the same as that of the image processing apparatus 3.

<5-1. Fitting result storage unit 125>
The fitting result storage unit 125 sequentially stores the borderline-attached frame image GF output from the shape model fitting unit 120 (see FIGS. 20 and 21).

  With the addition of the fitting result storage unit 125, the shape model change process can perform the following two model change processes. That is, the “first model change process” (the first model change process) for changing the shape model M used for the fitting process of the frame image G next to the frame image G from the shape model M subjected to the fitting process on the frame image G In addition to the first to third embodiments, refer to FIG. 20), and the shape model M subjected to the fitting process on the frame image G is changed to the shape model M used for the fitting process of the frame image G again. The “second model change process” (see FIG. 21) can be performed.

  Note that it is possible to modify the content of the boundary line (fitting function) F attached to the frame image GF with boundary line stored in the fitting result storage unit 125 by the second model change process. Become.

<5-2. Shape Model Suitability Evaluation Unit 135C>
The shape model appropriate evaluation unit 135C in the shape model control unit 130C is a boundary line attached to the frame model GF with a boundary line stored in the shape model M or the fitting result storage unit 125 sequentially changed in the shape model control unit 130C. Evaluation information is obtained based on (fitting function) F (see FIGS. 20 and 21).

  Further, in the shape model appropriate evaluation unit 135C, when the fps is simply fixed, simple evaluation information can be obtained because the change pattern of the shape model M is regular. However, assuming that there is a case where the change pattern is skipped when fps is insufficient or a case where the shape model M is fitted in the opposite direction in time, the time difference information between the frame images G to be processed is stored in advance. It is preferable to change the evaluation information based on the above.

  Furthermore, in the shape model appropriate evaluation unit 135C, the time change detection units 115B and 115B ′ in the third embodiment are added to obtain evaluation information based on heartbeat and respiratory phase information, It is also possible to obtain posture information of the subject OB from the camera image and obtain evaluation information based on body movement.

  Then, the shape model control unit 130C performs either the first model change process (see FIG. 20) or the second model change process (see FIG. 21) based on the evaluation information, and the changed shape. A command is given to the shape model fitting unit 120 so as to perform the fitting process using the model M.

  Hereinafter, an example of the evaluation information obtained by the shape model appropriate evaluation unit 135C will be described. Here, a case where the evaluation information is obtained based on the fitting function (boundary line) F attached to the frame image with boundary line GF stored in the fitting result storage unit 125 will be described.

  FIG. 22 is a schematic diagram for explaining whether or not the fitting function F attached to the frame image with boundary line GF is appropriate. 22 (a) and 22 (b) show functions f5 and f5e related to the shape of the lung apex, and FIGS. 22 (c) and 22 (d) show functions f7 and f7e related to the shape of the diaphragm.

  Since the function f5 shown in FIG. 22A appropriately represents the shape of the lung apex, it is evaluated that the fitting accuracy is high by the shape model appropriate evaluation unit 135C. On the other hand, since the function f5e shown in FIG. 22B uses a quartic function as the function for the shape of the apex of the lung, a part of the function f5e is convex downward, and the shape model appropriate evaluation unit 135C evaluates that the fitting accuracy is low. The

  Similarly, the function f7 shown in FIG. 22C is a case where a quadratic function is used, and appropriately expresses a convex shape on the diaphragm. For this reason, it is evaluated that the fitting accuracy is high by the shape model appropriate evaluation unit 135C. On the other hand, the function f7e shown in FIG. 22D is a case where a linear function (straight line) is used, and the convex shape on the diaphragm cannot be adequately expressed. For this reason, it is evaluated that the fitting accuracy is low by the shape model appropriate evaluation unit 135C.

  As described above, the shape model appropriate evaluation unit 135C evaluates the shape of the function f by evaluating whether the extreme value is within the allowable range or whether the convex shape appears depending on whether the extreme value is positive or negative. It is possible to evaluate by.

<5-3. Basic Operation of Image Processing Device 3C: First Model Change Process>
Next, FIG. 23 is a diagram illustrating an operation flow of the first model change process (see FIG. 20) in the image processing apparatus 3C according to the fourth embodiment. In FIG. 23, step S141 is the same as step S11 in FIG.

  In the fourth embodiment, the following process is changed by adding the fitting result storage unit 125 and the shape model appropriate evaluation unit 135C that did not exist in the first embodiment.

  That is, as a process similar to that of the first embodiment, after step S141, as shown in FIG. 23, in step S142, the shape model fitting unit 120 is a frame image with a boundary line as in step S12 (see FIG. 7). After obtaining GF, the borderline-attached frame image GF is output to the fitting result storage unit 125. Then, the fitting result storage unit 125 stores the boundary-attached frame image GF.

  In step S143, if there is no unprocessed process, the process proceeds to step S145. In step S145, the fitting result storage unit 125 outputs the stored frame image GF with a boundary line to the target region extraction unit 140. This operation flow is completed. On the other hand, if it is determined that there is unprocessed, the process proceeds to step S144.

  In step S144, the fitting function (boundary line) attached to the shape model M sequentially changed by the shape model appropriate evaluation unit 135C by the shape model control unit 130C or the frame image GF with boundary line stored in the fitting result storage unit 125 is stored. ) Obtain evaluation information based on F. Then, the shape model control unit 130C performs the first model change process based on the evaluation information, and gives a command to the shape model fitting unit 120 to perform the fitting process using the changed shape model M. The process proceeds to step S142.

  In step S <b> 142, the shape model fitting unit 120 performs the fitting process using the shape model M changed in step S <b> 144, and stores the boundary-attached frame image GF in the fitting result storage unit 125.

  As described above, the processes of Steps S142 to S144 are repeated until the fitting process is completed for all the frame images G. After the fitting process is completed for all the frame images G, the process goes through Step S145 described above. This operation flow is completed.

  As described above, in the shape model control unit 130C in the image processing apparatus 3C, the shape model M sequentially changed by the shape model control unit 130C or the frame image with a boundary line (region boundary information) stored in the fitting result storage unit 125 By performing the first model change process based on the evaluation information obtained based on the fitting function F in GF, it becomes possible to automatically change the more accurate shape model M. That is, the shape model control unit 130 </ b> C makes it possible to control the future change of the shape model M in time by performing the first model change process. Therefore, the lung field region extraction process can be performed more stably, and the extraction accuracy is further improved.

<5-4. Basic Operation of Image Processing Device 3C: Second Model Change Process>
Next, FIG. 24 is a diagram illustrating an operation flow of the second model change process (see FIG. 21) in the image processing apparatus 3C according to the fourth embodiment. In FIG. 24, step S241 is the same as step S11 in FIG.

  Similar to the first model change process, the following process is changed by adding the fitting result storage unit 125 and the shape model appropriate evaluation unit 135C that did not exist in the first embodiment.

  That is, as a process similar to that of the first embodiment, through step S241, as shown in FIG. 24, in step S242, the shape model fitting unit 120 is the frame image with a boundary line as in step S12 (see FIG. 7). The frame image with boundary line GF is sequentially output to the fitting result storage unit 125 while sequentially obtaining GF. Then, the fitting result storage unit 125 sequentially stores the boundary-attached frame image GF. In addition, it is preferable that the frame image GF with a boundary line obtained sequentially is acquired by the first model change process described above.

  In step S243, the fitting function (boundary line) F in the frame image GF with a boundary line stored in the shape model M or the fitting result storage unit 125 stored in the fitting model storage unit 125 or the shape model M sequentially changed by the shape model control unit 130C. Based on this, evaluation information is obtained.

  In step S244, the shape model control unit 130C determines whether or not to execute the second model change process based on the evaluation information acquired in step S243. In step S248, the fitting result storage unit 125 outputs the stored frame image with boundary line GF to the target region extraction unit 140, and the operation flow ends. Is done. On the other hand, when the second model change process is performed, the process proceeds to step S245.

  In step S245, the shape model control unit 130C performs the second model change process based on the evaluation information obtained in step S243, and performs the fitting process again using the changed shape model M. A command is given to the shape model fitting unit 120. At this time, the shape model control unit 130C gives a command to the fitting result storage unit 125 to output the frame image (frame image with boundary line GF) used for the fitting process again to the shape model fitting unit 120 (FIG. 21). Arrow AR1).

  In step S246, the shape model fitting unit 120 acquires the frame image with boundary line GF from the fitting result storage unit 125, and performs the fitting process again using the shape model M changed in step S245. Then, the boundary-attached frame image GF originally stored in the fitting result storage unit 125 is replaced with the boundary-attached frame image GF ′ output in the second fitting process and stored in the fitting result storage unit 125.

  Then, in step S247, the shape model control unit 130C determines whether or not to continue the second model change process, and if not, gives a command to end the fitting result storage unit 125 (arrow in FIG. 21). AR2), the operation flow is terminated through step S248 described above. On the other hand, when continuing the second model change process, the process proceeds to step S243.

  As described above, until the second model change process is completed, steps S243 to S247 are repeated. After the second model change process is completed, the operation flow is performed via the above-described step S248. Is terminated.

  As described above, in the shape model control unit 130C in the image processing apparatus 3C, the shape model M sequentially changed by the shape model control unit 130C or the frame image with a boundary line (region boundary information) stored in the fitting result storage unit 125 By implementing the second model change process based on the evaluation information obtained based on the boundary line (fitting function) F in GF, it becomes possible to automatically change the more accurate shape model M. . That is, the shape model control unit 130C can perform feedback control on the past shape model M that has already undergone the fitting process by performing the second model change process. Therefore, the lung field region extraction process can be performed more stably, and the extraction accuracy is further improved.

  In addition, you may perform the 1st and 2nd model change process mentioned above together. That is, the fourth embodiment includes a shape model control unit 130C that performs at least one of the first and second model change processes.

<6. Fifth Embodiment>
FIG. 25 is a diagram showing a functional configuration of a control unit 31D used in the image processing apparatus 3D (see FIG. 1) configured as the fifth embodiment of the present invention. The control unit 31D is used as an alternative to the control unit 31C (see FIG. 21) that controls the second model change process in the image processing apparatus 3C of the fourth embodiment. The difference from the fourth embodiment is that the shape model control unit 130D further includes a fitting function group storage unit 131D. The remaining configuration is the same as that of the image processing apparatus 3C (see FIG. 21).

<6-1. Fitting Function Group Storage Unit 131D>
In the fitting function group storage unit 131D, when the fitting result storage unit 125 stores three or more boundary-attached frame images GF, three or more boundary-attached frame images GF stored in the fitting result storage unit 125 are stored. A fitting function group indicating the fitting function F is stored.

  Then, the shape model appropriate evaluation unit 135D in the shape model control unit 130D obtains the fitting function group as evaluation information, so that the shape model control unit 130D performs the second model change process (see FIG. 25), and a command is given to the shape model fitting unit 120 to perform the fitting process again using the changed shape model M.

  Hereinafter, an example in which a fitting function group is used as evaluation information will be described. FIG. 26 is a schematic diagram for explaining the appropriate evaluation of the shape model M using the fitting function group. The upper part of FIG. 26 (fitting function F and the model that has determined the fitting function F) stores the fitting function F stored in the fitting function group storage unit 131D and the model that has determined the fitting function F (that is, the model that derives the fitting function F). FIG. 26 is a diagram illustrating the order, and the lower part of FIG. 26 (the shape model M after evaluation) shows the shape model M used in the second fitting process in the frame order after the second model change process is performed by the shape model control unit 130D. FIG.

  When the evaluation unit is three frame images G, as shown in FIG. 26, the first to third frame images G, the second to fourth frame images G, and the third to fifth frames. In the fourth frame image G and the fourth to sixth frame images G, the shape model appropriateness evaluation unit 135D evaluates and determines. Here, for example, when there is a criterion that the numbers indicating the fitting functions F1, F2, and F3 cannot decrease and increase, the fitting is performed in the third to fifth frame images G. Since it is recognized from the fitting function group that the function F is changed in the order of F2, F1, and F2, the fourth frame image G is changed to the fitting function F2 instead of the fitting function F1. Is obtained by the shape model appropriate evaluation unit 135D. Then, in response to the evaluation result by the shape model control unit 130D, the model that determines the fourth fitting function F is changed from the model 1 that derives the fitting function F1 to the model 2 that derives the fitting function F2. The second model change process is performed. For convenience of explanation, it is assumed that the fitting functions F1 to F3 are derived from the models 1 to 3.

  Thus, accuracy can be improved and stabilized by comprehensively determining a plurality of past fitting functions F in terms of time. Since the contents of the model do not change significantly in terms of time, if the judgment is made based on the relative evaluation of the fitting accuracy between the fitting functions F, the accuracy can be improved and stabilized.

<6-2. Basic Operation of Image Processing Device 3D: Second Model Change Process>
Subsequently, FIG. 27 is a diagram illustrating an operation flow of the image processing apparatus 3D (second model change process) according to the fifth embodiment. In FIG. 27, except for step S253, the process is the same as that of FIG.

  In the fifth embodiment, the following process is changed by adding the fitting function group storage unit 131D that did not exist in the fourth embodiment.

  That is, as a process similar to that of the fourth embodiment, through steps S251 to S252, as shown in FIG. 27, in step S253, the fitting function group storage unit 131D has the boundary lines sequentially stored in step S252. A fitting function group indicating three or more fitting functions F attached to the frame image GF is stored, and the shape model appropriate evaluation unit 135D obtains the fitting function group as evaluation information. The remaining steps are the same as in the fourth embodiment (see FIG. 24).

  As described above, in the image processing device 3D, the evaluation information includes the fitting function group indicating the three or more fitting functions F in the frame image GF with a boundary line stored in the fitting result storage unit 125. It is possible to determine the suitability between the fitting functions F and to perform the second model change process. Thereby, the fitting accuracy can be improved.

<7. Sixth Embodiment>
FIG. 28 is a diagram showing a functional configuration of the control unit 31E used in the image processing apparatus 3E (see FIG. 1) configured as the sixth embodiment of the present invention. The controller 31E can control both the first and second model change processes, and controls the first and second model change processes of the image processing apparatus 3C according to the fourth embodiment (see FIG. 20 and FIG. 21). The difference from the fourth embodiment is that the shape model control unit 130E further includes a fitting accuracy calculation unit 131E. Here, FIG. 28 is a diagram showing the first and second model change processes together. The remaining configuration is the same as that of the image processing apparatus 3C (see FIGS. 20 and 21).

<7-1. Fitting accuracy calculator 131E>
In the fitting accuracy calculation unit 131E, the boundary of the lung field area approximated by the fitting function F in the frame image with boundary line GF, and the frame image G used to derive the fitting function F (the frame image with boundary line GF) The image comparison information is calculated by comparing the difference between the boundary of the lung field region extracted from the image information using the predetermined method and the expected boundary candidate information.

  That is, the shape model fitting unit 120 extracts image information (edge or 0/1 region information) from the frame image G using a region growing method, a graph cut, a snake, or the like. (See FIG. 6), the boundary-attached frame image GF2 (see FIG. 6) obtained by adding the image information to the boundary-attached frame image GF is stored in the fitting result storage unit 125. The boundary-attached frame image GF2 is output from the fitting result storage unit 125 to the fitting accuracy calculation unit 131E, and image comparison information is calculated by the fitting accuracy calculation unit 131E.

  When the fitting process is performed on the edge of the frame image G for the frame image GF2 with the boundary line, between the fitting function F and the edge (extracted by a separate image extraction process (predetermined method)). The generated deviation can be calculated, and the distance of the deviation can be used as image comparison information. In addition, when the fitting process is performed on the 0/1 region information extracted from the frame image G by the frame image GF2 with the boundary line, the edge is clear at 0/1, and thus a relatively simple image extraction process ( An edge can be extracted using a predetermined method, and image comparison information can be obtained by calculating a fitting function and a distance between edges. Further, when the fitting process is performed on the frame image G having the density of the frame image with boundary line GF2 based on the edge likeness of each pixel, the weighting is performed according to the edge likeness of each pixel. The image comparison information can be obtained by calculating the distance between the edge extracted using the image extraction process (predetermined method) and the fitting function F in consideration. The image comparison information may be the distance between the X and Y coordinates, or may be only one of the X or Y coordinates for simplicity. In addition, when the direction in which the shape model M is applied is one direction, it can be easily calculated by making it the same as that direction.

  Then, the shape model appropriate evaluation unit 135E in the shape model control unit 130E obtains the image comparison information as evaluation information, so that the shape model control unit 130E can change the first and second models based on the evaluation information. At least one of the processes (see FIG. 28) is performed, and a command is given to the shape model fitting unit 120 to perform the fitting process using the changed shape model M.

  Hereinafter, an example in which image comparison information is used as evaluation information will be described. FIG. 29 is a schematic diagram for explaining the appropriate evaluation of the shape model M using the image comparison information. In FIG. 29 (a), as in FIG. 18 (b), the frame image Gb corresponding to an intermediate position between expiration and inspiration in the change in the respiratory cycle, and the boundary P4 of the diaphragm region corresponding to the lung field region adjacent region is shown. The function f72 to be expressed is a diagram showing that the fitting process is performed as a quadratic function. FIG. 29B shows a frame image Gc (similar to FIG. 18C) corresponding to the intake air after a predetermined number of frames have elapsed from the frame image Gb of FIG. 29A, and the frame image at the boundary P4. It is a figure which shows the result of having fitted fitting function f72 (namely, quadratic function) similar to Gb as a test, and in FIG.29 (c), function f73 (namely, cubic function) in the boundary P4 of the frame image Gc. It is a figure which shows the result of having fitted. In addition, the area | region IR shown with the dot shows the area | region judged to be a lung field area | region from image information similarly to FIG.

  As shown in FIG. 29, when the fitting process is performed on the frame image Gc using the function f72 (quadratic function) with good fitting accuracy in the frame image Gb (see FIG. 29A), the fitting accuracy is improved. It becomes worse (see FIG. 29B). That is, the fitting accuracy calculation unit 131E automatically calculates the image comparison information. Here, the difference value is calculated to be small as image comparison information for the diaphragm region of the frame image Gb in FIG. 29A, and conversely, the difference value is calculated for the frame image Gc in FIG. Is greatly calculated. Then, the shape model appropriate evaluation unit 135E determines that the function f of the cubic function is more appropriate than the quadratic function for the boundary P4 of the diaphragm region of the frame image Gc in FIG. The controller 130E performs the first or second model change process for changing from the function f72 to the function f73.

<7-2. Basic Operation of Image Processing Device 3E: First Model Change Process>
Subsequently, FIG. 30 is a diagram illustrating an operation flow of the first model change process (see FIG. 28) in the image processing apparatus 3E according to the sixth embodiment. In FIG. 30, steps S161 and S163 are the same as steps S141 and S143 in FIG.

  In the sixth embodiment, the following steps are changed by adding the fitting accuracy calculation unit 131E that did not exist in the fourth embodiment.

  That is, as a process similar to that of the fourth embodiment, after step S161, as shown in FIG. 30, in step S162, the shape model fitting unit 120 attaches a boundary line with image information added to the frame image GF with a boundary line. The frame image GF2 is output to the fitting result storage unit 125, and the fitting result storage unit 125 stores the boundary-attached frame image GF2.

  In step S163, if there is no unprocessed state, the process proceeds to step S166. In step S166, the fitting result storage unit 125 removes image information from the stored frame image GF2 with a boundary line. The attached frame image GF is output to the target area extraction unit 140, and this operation flow is terminated. On the other hand, if it is determined that there is an unprocessed state, the process proceeds to step S164.

  In step S164, the fitting result storage unit 125 outputs the frame image GF2 with a boundary line to the fitting accuracy calculation unit 131E, and the fitting accuracy calculation unit 131E calculates image comparison information based on the frame image with a boundary line GF2.

  In step S165, the shape model appropriate evaluation unit 135E obtains evaluation information based on the image comparison information. Then, the shape model control unit 130E performs the first model change process based on the evaluation information, and gives a command to the shape model fitting unit 120 to perform the fitting process using the changed shape model M. Then, the process proceeds to step S162.

  In step S162, the shape model fitting unit 120 performs a fitting process using the shape model M changed in step S165, and stores the frame image GF2 with a boundary line in the fitting result storage unit 125.

  As described above, the processes in steps S162 to S165 are repeated until the fitting process is completed for all the frame images G. After the fitting process is completed for all the frame images G, the process goes through the above-described step S166. This operation flow is completed.

  As described above, in the image processing device 3E, the evaluation information includes the boundary of the lung field region that is approximated by the fitting function F in the frame image GF with a boundary line, and the frame image G used to derive the fitting function F (the relevant By including image comparison information that compares the difference between the boundary of the lung field extracted from the image information of the frame image with boundary line GF) using a predetermined method and the expected boundary candidate information, a predetermined method is separately obtained. Since the first model change process can be performed with reference to the difference from the actual image information obtained by using, the fitting accuracy can be improved.

<7-3. Basic Operation of Image Processing Device 3E: Second Model Change Process>
Next, FIG. 31 is a diagram illustrating an operation flow of the second model change process (see FIG. 28) in the image processing apparatus 3E according to the sixth embodiment. In FIG. 31, steps S261, S264, and S267 are the same as steps S241, S244, and S247 in FIG.

  Similar to the first model change process, the following steps are changed by adding the fitting accuracy calculation unit 131E that did not exist in the fourth embodiment.

  That is, as a process similar to the fourth embodiment, after step S261, as shown in FIG. 31, in step S262, the shape model fitting unit 120 sequentially obtains the frame image GF2 with a boundary line while obtaining the boundary line-attached image. The frame image GF2 is sequentially output to the fitting result storage unit 125. Then, the fitting result storage unit 125 sequentially stores the boundary-attached frame image GF2. In addition, it is preferable that the frame image GF2 with a boundary line obtained sequentially is acquired by the above-described first model change process (see FIG. 30).

  In step S263, the fitting result storage unit 125 sequentially outputs the frame image with boundary line GF2 to the fitting accuracy calculation unit 131E, and the fitting accuracy calculation unit 131E sequentially calculates image comparison information based on the frame image with boundary line GF2.

  In step S264, based on the image comparison information acquired in step S263, it is determined whether or not the second model change process is to be performed. If not, the process proceeds to step S268. In step S268, the fitting is performed. The result storage unit 125 outputs the frame image with boundary line GF from which the image information is removed from the stored frame image with boundary line GF2 to the target area extraction unit 140, and the operation flow is ended. On the other hand, when the second model change process is performed, the process proceeds to step S265.

  In step S265, the shape model control unit 130E performs the second model change process based on the image comparison information obtained in step S263, and performs the fitting process again using the changed shape model M. Thus, a command is given to the shape model fitting unit 120. At this time, the shape model control unit 130E gives a command to the fitting result storage unit 125 so as to output the frame image (frame image with boundary line GF2) used for the fitting process again to the shape model fitting unit 120.

  In step S266, the shape model fitting unit 120 acquires the frame image with boundary line GF2 from the fitting result storage unit 125, and performs the fitting process again using the shape model M changed in step S245. Then, the boundary-attached frame image GF2 originally stored in the fitting result storage unit 125 is replaced with the boundary-attached frame image GF2 output in the second fitting process, and stored in the fitting result storage unit 125.

  Then, in step S267, the shape model control unit 130E determines whether or not to continue the second model change process. If not, the process moves to step S268, and the operation flow passes through the above step S268. Is terminated. On the other hand, when the second model change process is further continued, the process proceeds to step S263.

  As described above, until the second model change process is completed, steps S263 to S267 are repeated. After the second model change process is completed, the operation flow is performed via the above-described step S268. Is terminated.

  As described above, in the image processing device 3E, the evaluation information includes the boundary of the lung field region that is approximated by the fitting function F in the frame image GF with a boundary line, and the frame image G used to derive the fitting function F (the relevant By including image comparison information that compares the difference between the boundary of the lung field extracted from the image information of the frame image with boundary line GF) using a predetermined method and the expected boundary candidate information, a predetermined method is separately obtained. Since the second model change process can be performed with reference to the difference from the actual image information obtained by using, the fitting accuracy can be improved.

<8. Seventh Embodiment>
FIG. 32 is a diagram illustrating a functional configuration of a control unit 31F used in the image processing device 3F (see FIG. 1) configured as the seventh embodiment of the present invention. The control unit 31F can control the first model change process, and is an alternative to the control unit 31C (see FIG. 20) that controls the first model change process in the image processing apparatus 3C of the fourth embodiment. used. The difference from the fourth embodiment is that the shape model control unit 130F further includes a fitting history storage unit 131F. The remaining configuration is the same as that of the image processing apparatus 3C (see FIG. 20).

<8-1. Fitting history storage unit 131F>
In the fitting history storage unit 131F, the shape model M sequentially determined by the shape model control unit 130F is stored in order and stored collectively as fitting history information.

  Then, the shape model appropriate evaluation unit 135F in the shape model control unit 130F obtains the fitting history information as evaluation information, so that the shape model control unit 130F performs the first model change process (FIG. 32) and a command is given to the shape model fitting unit 120 to perform the fitting process using the changed shape model M.

  Hereinafter, an example in which fitting history information is used as evaluation information will be described. FIG. 33 is a schematic diagram for explaining the appropriate evaluation of the shape model M using the fitting history information. The upper part of FIG. 33 (fitting function F and the model that has determined it) shows the fitting function F stored in the fitting history storage unit 131F and the model that has determined it (that is, the model that derives the fitting function F) in the order of frames. The lower part of FIG. 33 (fitting history) is a diagram showing the history information of the shape model M already stored in the fitting history storage unit 131F. The shape model M shown in the fitting history is only a model determined to be appropriate by the shape model appropriate evaluation unit 135F, and is stored in the fitting history storage unit 131F.

  As shown in FIG. 33, the frame image G (frame (1) to frame (i)) from the past to the present is processed by the same model as the model in which the fitting function F is determined and the model shown in the fitting history. If it is, the next frame image G (frame (i + 1)) can be predicted to be the same as the fitting history. Therefore, the shape model appropriate evaluation unit 135F gives high evaluation to adopting the model 2 for deriving the fitting function F2 for the frame (i + 1), and adopts the model 1 and the model 3 for deriving the fitting functions F1 and F3. Give a low rating to do. Then, the shape model control unit 130F receives the evaluation information, and performs a so-called first model change process in which the shape model F of the frame (i + 1) is changed from the model 3 to the model 2. For convenience of explanation, it is assumed that the fitting functions F1 to F3 are derived from the models 1 to 3.

  In this way, the change of the shape model M can be evaluated using the change history of the shape model M used for obtaining the past frame image GF with a boundary line. By using the already determined model history, it becomes possible to achieve stabilization while maintaining consistency with the past frame image GF with a boundary line.

<8-2. Basic Operation of Image Processing Device 3F: First Model Change Process>
Next, FIG. 34 is a diagram illustrating an operation flow of the image processing device 3F (see FIG. 32) according to the seventh embodiment. In FIG. 34, steps S171 to S173 and S175 are the same as steps S141 to S143 and S145 in FIG.

  In the seventh embodiment, the following process is changed by adding the fitting history storage unit 131F that did not exist in the fourth embodiment.

  That is, as steps similar to those in the fourth embodiment, after steps S171 to S173, as shown in FIG. 34, in step S174, after the shape model appropriate evaluation unit 135F obtains the evaluation information based on the fitting history information. The shape model control unit 130F performs a first model change process based on the evaluation information, and gives a command to the shape model fitting unit 120 to perform the fitting process using the changed shape model M, The process moves to step S172. The remaining steps are the same as in the fourth embodiment.

  As described above, in the image processing apparatus 3F, since the evaluation information includes the fitting history information, the first model changing process can be performed while referring to the fitting history information, so that the fitting accuracy can be improved. To do.

<9. Eighth Embodiment>
FIG. 35 is a diagram illustrating a functional configuration of a control unit 31G used in the image processing apparatus 3G (see FIG. 1) configured as the eighth embodiment of the present invention. The control unit 31G is used as an alternative to the control unit 31C (see FIG. 21) that controls the second model change process in the image processing apparatus 3C of the fourth embodiment. The difference from the fourth embodiment is that the shape model control unit 130G further includes a position shape change calculation unit 131G. The remaining configuration is the same as that of the image processing apparatus 3C (see FIG. 21).

<9-1. Position / Shape Change Calculation Unit 131G>
In the position shape change calculation unit 131G, a position shape indicating a temporal position displacement amount or shape change of the lung field region adjacent region between the fitting functions F in the frame image with boundary line GF stored in the fitting result storage unit 125. Change information is calculated.

  Then, the shape model appropriate evaluation unit 135G in the shape model control unit 130G obtains the position shape change information as evaluation information, so that the shape model control unit 130G performs the second model change process ( 35), and a command is given to the shape model fitting unit 120 to perform the fitting process again using the changed shape model M.

  Below, an example which uses position shape change information as evaluation information is demonstrated. FIG. 36 is a schematic diagram for explaining the proper evaluation of the shape model using the position shape change information. The boundary P3 of the diaphragm region of the fitting function F stored in the fitting result storage unit 125 (see FIG. 4). FIG. As shown in FIGS. 5A and 5B, the position shape change information between f12 and f11, between f13 (f13e) and f12, between f14 (f14e) and f13, and between f15 and f14 is the position shape change information. Become.

  First, the position shape change information indicating the position displacement amount will be described. As shown in FIG. 36A, among the first to fifth frame images G (frames (1) to (5)), the functions f11 and f12 of the frames (1) to (3) and (5). , F13, and f15 indicate trends whose positions change to the right in this order. However, the function f14e of the frame (4) does not exist at the middle position between the functions f13 and f15, and is located below the position of the function f13. For this reason, it is preferable that the fourth frame image G is changed from the function f14e to the position of the function f14. The evaluation result based on the position shape change information mainly based on the position displacement amount is the shape model appropriate evaluation unit 135G. Is obtained. The shape model control unit 130G receives the evaluation result, and changes the boundary P3 of the diaphragm region of the shape model M of the fourth frame image G from the function f14e to the function f14, so-called second model change. Perform the process.

  Next, position shape change information indicating a shape change will be described. As shown in FIG. 36B, among the first to fifth frame images G, the functions f11, f12, f14, and f15 of the frames (1), (2), (4), and (5) are convex upward. However, only the function f13e of the frame (3) expresses a downwardly convex shape. For this reason, it is preferable that the third frame image G is changed from the function f13e to the shape of the function f13, and an evaluation result based on position shape change information mainly including shape change is obtained by the shape model appropriate evaluation unit 135G. can get. Then, upon receiving the evaluation result, the shape model control unit 130G changes the boundary P3 of the diaphragm region of the shape model M of the third frame image G from the function f13e to the function f13, so-called second model change. Perform the process.

<9-2. Basic Operation of Image Processing Device 3G: Second Model Change Process>
Subsequently, FIG. 37 is a diagram illustrating an operation flow of the image processing apparatus 3G (second model change process) according to the eighth embodiment. In FIG. 37, except for step S283, the process is the same as that of FIG.

  In the eighth embodiment, the following process is changed by adding the position shape change calculating unit 131G that did not exist in the fourth embodiment.

  That is, as a process similar to that of the fourth embodiment, through step S281 to step S282, as shown in FIG. 37, in step S283, the position shape change calculating unit 131G has the boundary line sequentially stored in step S282. Between the fitting functions F attached to the frame image GF, position shape change information indicating the temporal position displacement amount and shape change of the lung field adjacent region is calculated, and the shape model appropriate evaluation unit 135G receives the position shape change information. Is obtained as evaluation information (see FIG. 36). The remaining steps are the same as in the fourth embodiment (see FIG. 24).

  As described above, in the image processing device 3G, since the evaluation information is position shape change information, if the position shape change is large, it is possible to predict that the fitting accuracy is poor and to perform the second model change process. Therefore, the fitting accuracy can be improved. Conversely, if the change in position and shape is small, it can be predicted that the fitting accuracy will not be bad. Therefore, a determination not to perform the second model change process ("N" in step S283) can be made. Thereby, it becomes possible to prevent a fitting precision deterioration.

<10. Ninth Embodiment>
FIG. 38 is a diagram showing a functional configuration of the control unit 31H used in the image processing apparatus 3H (see FIG. 1) configured as the ninth embodiment of the present invention. The control unit 31H can control both the first and second model change processes, and controls the first and second model change processes of the image processing apparatus 3C according to the fourth embodiment (see FIG. 20 and FIG. 21). The difference from the fourth embodiment is that the shape model control unit 130H further includes a positional relationship calculation unit 131H. Here, FIG. 38 is a diagram showing the first and second model change processes together. The remaining configuration is the same as that of the image processing apparatus 3C (see FIGS. 20 and 21).

<10-1. Positional Relationship Calculation Unit 131H>
In the case where there are a plurality of adjacent lung field areas, the positional relationship calculation unit 131H indicates a relative positional relationship between the estimated adjacent areas of the lung field area of the fitting function F attached to the frame image with boundary line GF. Calculate relationship information.

  Then, the shape model appropriate evaluation unit 135H in the shape model control unit 130H obtains the positional relationship information as evaluation information, so that the shape model control unit 130H can change the first or second model based on the evaluation information. The processing (see FIG. 38) is performed, and a command is given to the shape model fitting unit 120 to perform the fitting processing using the changed shape model M.

  Examples of the case where the positional relationship information is used as evaluation information include the following. For example, the shape of the diaphragm and heart changes according to respiration and heartbeat, but the right and left lung fields have the same normal phase and are linked. If the positional relationship of the diaphragms is significantly different on the left and right, the evaluation value can be lowered because the fitted shape model M may be bad. On the other hand, the bulge position of the heart moves during the heartbeat, but the positional relationship in the height direction does not change much between the left lung field and the right lung field. Therefore, the evaluation value can be increased as the positional relationship is the same.

  FIG. 39 is a schematic diagram for explaining the appropriate evaluation of the shape model M using the positional relationship information. Here, the diaphragm region (the height of the left lung field and the right lung field is used as the lung field region proximity region). (Directional positional relationship) will be described as an example. FIG. 39A is a diagram showing the functions f1e and f7e at the boundaries P3 and P4 (see FIG. 4) of the diaphragm region where the fitting process has not been properly performed, and FIG. 39B shows the evaluation results. It is a figure which shows the function f1, f7 by which fitting process was performed appropriately.

  As shown in FIG. 39 (a), the functions f1e and f7e are subjected to the fitting process in a state where the positional relationship in the height direction of the boundaries P3 and P4 (see FIG. 4) of the diaphragm region is greatly different on the left and right. Therefore, the shape model appropriate evaluation unit 135H obtains an evaluation result that it is preferable to change to an appropriate positional relationship (functions f1, f7) as shown in FIG. Then, upon receiving the evaluation result, the shape model control unit 130H changes the functions f1e and f7e of the shape model M of the frame image G to functions f1 and f7 (see FIG. 39B), so-called first or A second model change process is performed.

<10-2. Basic Operation of Image Processing Device 3H: First Model Change Process>
Subsequently, FIG. 40 is a diagram illustrating an operation flow of the first model change process (see FIG. 38) in the image processing apparatus 3H according to the ninth embodiment. In FIG. 40, steps S191 to S193 and S196 are the same as steps S141 to S143 and S145 of FIG.

  In the sixth embodiment, the following steps are changed and added by adding the positional relationship calculation unit 131H that did not exist in the fourth embodiment.

  That is, as a process similar to the first embodiment, through step S191 to step S193, as shown in FIG. 40, in step S194, the positional relationship calculation unit 131H stores the frame image with a boundary line stored in step S192. The positional relationship information indicating the relative positional relationship between the estimated proximity regions of the lung field region of the fitting function F attached to GF is calculated (see FIG. 39).

  In step S195, the shape model appropriate evaluation unit 135H obtains an evaluation result based on the positional relationship information. Then, the shape model control unit 130H performs a first model change process based on the evaluation result, and gives a command to the shape model fitting unit 120 to perform the fitting process using the changed shape model M. Then, the process proceeds to step S192. The remaining steps are the same as in the fourth embodiment (see FIG. 23).

  As described above, in the image processing device 3H, the evaluation information is positional relationship information, and therefore, when the relative positional relationship between the estimated proximity regions of the lung field region is different from the assumption, it is predicted that the fitting accuracy is poor. Since the first model change process can be performed, the fitting accuracy can be improved.

  Conversely, when the relative positional relationship between the estimated proximity regions of the lung field region is within the assumption, it can be predicted that the fitting accuracy is not bad. For this reason, in the first model change process, a process that does not greatly change the shape model M or a process in which the current shape model M is used as it is as the future shape model M can be performed. Thereby, it becomes possible to prevent a fitting precision deterioration.

<10-3. Basic Operation of Image Processing Device 3H: Second Model Change Process>
Subsequently, FIG. 41 is a diagram illustrating an operation flow of the second model change process (see FIG. 38) in the image processing apparatus 3H according to the ninth embodiment. 41, except for step S293, is the same as the process of FIG. 24, and thus the description thereof is omitted.

  Similar to the first model change process, the following steps are changed by adding the positional relationship calculation unit 131H that did not exist in the fourth embodiment.

  That is, as a process similar to that of the fourth embodiment, through steps S291 to S292, as shown in FIG. 41, in step S293, the positional relationship calculation unit 131H sequentially stores the frame with a boundary line stored in step S292. In the fitting function F attached to the image GF, the positional relationship information indicating the relative positional relationship between the estimated proximity regions of the lung field region is sequentially calculated for each frame, and the shape model appropriate evaluation unit 135H receives the positional shape change information. Is obtained as evaluation information (see FIG. 39). The remaining steps are the same as in the fourth embodiment (see FIG. 24).

  As described above, the image processing apparatus 3G predicts that the fitting accuracy is poor when the relative positional relationship between the estimated adjacent regions of the lung field region is different from the assumption because the evaluation information is the positional relationship information. Since the second model change process can be performed, the fitting accuracy can be improved. Conversely, when the relative positional relationship between the estimated proximity regions of the lung field region is within the assumption, it can be predicted that the fitting accuracy is not bad. Therefore, it is possible to make a determination that the second model change process is not performed (“N” in step S294). Thereby, it becomes possible to prevent a fitting precision deterioration.

<11. 10th Embodiment>
FIG. 42 is a diagram showing a functional configuration of the control unit 31I used in the image processing apparatus 3I (see FIG. 1) configured as the tenth embodiment of the present invention. The control unit 31I can control the first model change process, and is an alternative to the control unit 31C (see FIG. 20) that controls the first model change process in the image processing apparatus 3C of the fourth embodiment. used. The difference from the fourth embodiment is that a time change detection unit 115I is further provided, and that the shape model control unit 130I is further provided with a priority change pattern storage unit 131I. The remaining configuration is the same as that of the image processing apparatus 3C (see FIG. 20).

<11-1. Time Change Detection Unit 115I>
The time change detection unit 115I detects a periodic time change in the lung field adjacent region synchronized with the frame image G, and obtains time change information T. The time change detection unit 115I has the same function as the time change detection unit 115B (see FIG. 10) in the third embodiment, and may be replaced with the function of the time change detection unit 115B ′ (see FIG. 11). Is possible.

<11-2. Change pattern storage unit 131I with priority>
The change pattern storage unit with priority 131I stores change pattern information with priority C created in advance by assigning priority to the change pattern of the shape model M corresponding to the periodic time change of the lung field adjacent region. To do.

  Periodic changes over time in the adjacent region of the lung field in the change pattern of the shape model M include changes in the heart shape due to the heartbeat phase (see FIGS. 13 and 14), concealment of the diaphragm due to the respiratory phase (see FIG. 18), and the aorta. The hiding of the. For example, as shown in FIG. 18, the diaphragm changes in order with inspiration and decreases in order with inspiration, so that the change destination of the shape model M is limited in advance corresponding to the phase. It is possible to set the evaluation condition as the change pattern information with priority C. In other words, since the lungs expand in the inspiratory phase and approach the state where the shape of the heel lateral angle can be seen well, an evaluation condition for transitioning in the direction in which the order of the diaphragm function increases is set as the priority change pattern information C. On the contrary, since the lung contracts and hides in other organs in the expiration phase, an evaluation condition that makes a transition in the direction of decreasing the order can be set as the change pattern information C with priority.

  Then, the shape model appropriate evaluation unit 135I in the shape model control unit 130I obtains the change pattern information with priority C as evaluation information, so that the shape model control unit 130I uses the first model based on the evaluation information. The change process (see FIG. 42) is performed, and a command is given to the shape model fitting unit 120 to perform the fitting process using the changed shape model M.

  Below, an example which uses the change pattern information C with a priority as evaluation information is demonstrated. FIG. 43 is a schematic diagram for explaining the appropriate evaluation of the shape model using the change pattern information with priority. Here, the lung region adjacent region is a diaphragm region, and the periodic time change is exhaled breath. Will be described.

  As shown in FIG. 43, when the shape model M of the current frame image G is ID1, if the shape model M of the next frame image G is ID1, both exhalation and inspiration are 5 with the highest priority evaluation. Become. If the shape model M of the next frame image G is ID2, exhalation has a medium priority evaluation of 3, and inhalation has a lowest priority evaluation of 0. If the shape model M of the next frame image G is ID3, exhalation has a low priority evaluation of 1, and inhalation has a lowest priority evaluation of 0. For example, when it is known from the time change information T obtained by the time change detection unit 115I that the next frame image G is located in the expiration, the shape model appropriate evaluation unit 135I has the shape model M of the current frame image G of ID1. In some cases, high evaluation is given to adopting ID1 as the shape model M of the next frame image G, and low evaluation is given to adopting ID2 or ID3. The shape model control unit 130 receives the evaluation information, and performs a so-called first model change process in which the shape model M is changed (maintained) from ID1 to ID1.

  Similarly, when the shape model M of the current frame image G is ID2, if the shape model M of the next frame image G is ID1, exhalation has the lowest priority evaluation of 0, and inhalation has a medium priority evaluation. It becomes 3. If the shape model M of the next frame image G is ID2, both exhalation and inspiration are 5 with the highest priority evaluation. For example, when it is known from the time change information obtained by the time change detection unit 115I that the next frame image G is located in the intake air, the shape model appropriate evaluation unit 135I determines that the shape model M of the current frame image G is ID2. Adopting ID2 as the shape model M of the next frame image G gives high evaluation, and adopting ID1 gives low evaluation. The shape model control unit 130I receives the evaluation information, and performs a so-called first model change process in which the shape model M is changed (maintained) from ID2 to ID2.

<11-3. Basic Operation of Image Processing Device 3I: First Model Change Process>
Subsequently, FIG. 44 is a diagram illustrating an operation flow of the image processing apparatus 3I (see FIG. 42) according to the tenth embodiment. In FIG. 44, steps S102, S103, and S105 are the same as steps S142, S143, and S145 in FIG.

  The following steps are changed by adding the priority-added change pattern storage unit 131I that did not exist in the fourth embodiment.

  As shown in FIG. 44, first, in step S101, the moving image acquisition unit 110 of the control unit 31I acquires the moving image captured by the reading control device 14 of the imaging device 1 via the imaging control device 2. The time change detection unit 115I acquires the respiratory cycle change from the cycle detection unit 15 of the imaging device 1, and acquires the heartbeat cycle change (time change information C) from the phase detection unit 41 of the electrocardiograph 4.

  Then, as steps similar to the fourth embodiment, through steps S102 to S103, as shown in FIG. 44, in step S104, the shape model appropriate evaluation unit 135I associates with the time change acquired in step S101. After obtaining the evaluation information based on the obtained priority-added change pattern information C (see FIG. 43), the shape model control unit 130I performs the first model change processing based on the evaluation information, and has been changed. A command is given to the shape model fitting unit 120 to perform the fitting process using the shape model M, and the process proceeds to step S102. The remaining steps are the same as in the fourth embodiment.

  As described above, in the image processing device 3I, since the evaluation information is the change pattern information with priority C associated with the time change, the first model change process is performed with reference to the change pattern information with priority C. It becomes possible to carry out. As a result, the shape model can be changed in conformity with the periodic change of the lung field region adjacent region (diaphragm region or the like), so that the fitting accuracy can be improved.

<12. Method for processing frame image G>
In the processing method in which the frame image G included in the moving image is input to the shape model fitting unit 120 and the frame image with boundary line GF is output to the target region extraction unit 140, the image processing apparatus of the present invention can be broadly divided into the following three. One case is mainly assumed.

(I) Processing for each frame image G The first is input to the shape model fitting unit 120 for each frame image G and output to the target region extraction unit 140 for each frame image GF with a boundary line. It is processing to do. That is, the process is performed for each frame image G, and the image comparison information (see FIG. 29), fitting history information (see FIG. 33), and positional relationship information (see FIG. 39) described in the sixth, seventh, ninth, and tenth embodiments. The first model change process is performed based on the evaluation information such as the change pattern information with priority (see FIG. 43). In this case, since only the processing information so far needs to be held, it is possible to simply process each successive frame image G.

(II) Moving Image Data Batch Processing Second, all the frame images G included in the moving image are input to the shape model fitting unit 120 at the same time (however, the fitting processing is sequentially performed), and the frame image GF with a boundary line is input. Are collectively output to the target area extraction unit 140. That is, this is a moving image data batch process. After the fitting process, all the frame images with boundary lines GF are stored in the fitting result storage unit 125, and then the fitting function group described in the fifth, sixth, eighth, and ninth embodiments (FIG. 26). The second model change process is performed based on evaluation information such as reference), image comparison information (see FIG. 29), position shape change information (see FIG. 36), positional relationship information (see FIG. 39), and the like. In this case, it is possible to sufficiently use information in the time direction by buffering the data of all the frame images with boundary lines GF in the fitting result storage unit 125.

(III) Extrusion processing Thirdly, the X1 number of frame images G included in the moving image are input to the shape model fitting unit 120, and the X2 number of the frame images G out of the X1 number of frame images G are subjected to the fitting processing, and sequentially. Specifically, the frame image with boundary line GF is output to the target area extraction unit 140.

  For example, when 30 frame images G included in a moving image are input to the shape model fitting unit 120, the fitting process is performed without using time direction information until the first to fifth frame images G. The fitting process is performed on the sixth to 25th frame images G after the first model change process using the fitting process results (time information) of the first to fifth frame images G. After the 26th frame image G, the fitting process is not performed and the frame image G is held.

  Next, when 30 sheets from the 31st to 60th frame images G are input to the shape model fitting unit 120, the 26th to 55th frame images G are fitted to the 1st to 25th frame images G. A fitting process is performed after a 1st model change process using a process result (time information). Then, after the 56th frame image G, the fitting process is not performed and is held.

  Thereafter, if it is determined that there is no subsequent frame image G, the fitting process is performed for the 56th to 60th frame images G without using information in the time direction.

  In the above processing example, the processing is performed every 30 frames, but may be performed for each frame image G. In this processing method, the image comparison information (see FIG. 29), the fitting history information (see FIG. 33), the positional relationship information (see FIG. 39), and the priority change described above in the sixth, seventh, ninth, and tenth embodiments. In addition to the first model change process based on evaluation information such as pattern information (see FIG. 43), the fitting function group (see FIG. 26) and image comparison information (see FIG. 29) described in the fifth, sixth, eighth, and ninth embodiments. Reference model), position shape change information (see FIG. 36), positional relationship information (see FIG. 39), and other second model change processing based on evaluation information can also be implemented. In addition, this processing that can be processed sequentially and has little time loss is suitable for real-time processing.

<13. Modification>
As mentioned above, although embodiment of this invention has been described, this invention is not limited to the said embodiment, A various deformation | transformation is possible.

  * In the present embodiment, the image processing devices 3, 3A, 3B, 3B ', 3C-3I are described separately for each embodiment so that they are individually implemented. You may combine with each other.

  * The fitting process (shape model fitting unit 120) in the present embodiment is configured to sequentially output the frame image GF with a boundary line, but is not limited to this, and “an image obtained by cutting out a lung field region (extraction region)” Or “only fitting function F” may be output. Therefore, the “frame image with boundary line GF” is pointed out as the predetermined area boundary information that reflects at least the fitting function F in the present embodiment, but the present invention is not limited to this. F ″ may also be used.

  In the present embodiment, the fitting function F is expressed (reflected) by “the boundary line attached to the frame image GF with boundary line”, but the predetermined area boundary information is “an image obtained by cutting out the extraction area”. In this case, the fitting function F is expressed (reflected) by “the edge of the image from which the extraction region is cut out”.

  * In this invention, the lung field area where the physical state changes periodically with time is extracted from the part to be photographed, but it is not only the lung field area but also involuntary movements such as peristalsis. It may be other organs that perform the movement, or may be an area where voluntary movements such as muscles and joints are performed. In addition, the present invention is not limited to the visceral regions such as the lung field and the heart, and for example, a foreign substance entering the body may be the extraction target.

  * The subject may be an animal body as well as a human body.

DESCRIPTION OF SYMBOLS 1 Image pick-up device 2 Image pick-up control device 3, 3A, 3B, 3B ', 3C-3I Image processing device 4 Electrocardiograph 31, 31A, 31B, 31B', 31C-31I Control part 41 Phase detection part 100, 100A, 100B, 100B ′, 100C to 100I Radiodynamic imaging system 110 Moving image acquisition unit 120 Shape model fitting unit 115B, 115B ′, 115I Time change detection unit 131D Fitting function group storage unit 131E Fitting accuracy calculation unit 131F Fitting history storage unit 131G Position shape Change calculation unit 131H Position relation calculation unit 131I Change pattern storage unit with priority 135A Constraint storage unit 135C to 135I Shape model appropriate evaluation unit 140 Target region extraction unit M Shape model F Fitting function G Frame image GF Lame image OB Subject (subject)

Claims (17)

A moving image acquisition means for acquiring a moving image that sequentially captures a state in which a physical state of a predetermined region in a human body or an animal changes with time;
By sequentially obtaining a plurality of frame images included in the moving image, and performing a fitting process for fitting a shape model expressing the global shape of the predetermined area on the frame image, Determining a fitting function that approximates the boundary of the predetermined region in the shape model fitting means for sequentially obtaining predetermined region boundary information reflecting at least the fitting function;
Shape model control means for performing shape model change processing for sequentially changing the shape model used for the fitting processing on the frame image along the time direction;
With
The fitting function is composed of a predetermined number of functions,
The shape model is
A model for determining the fitting function, characterized in that it includes at least one information out of the predetermined number and an allowable range of the order for each function,
Image processing device. The image processing apparatus according to claim 1,
The shape model is
Information on the fitting application range for each function on the frame image;
Further including
Image processing device. The image processing apparatus according to claim 2,
The shape model change process includes:
(A) a process of increasing or decreasing the predetermined number;
(B) a process of changing an allowable range of the order of at least one function among the predetermined number of functions;
(C) a process of changing the fitting application range of at least one function among the predetermined number of functions;
Including at least one process,
Image processing device. The image processing apparatus according to claim 3,
The shape model fitting means includes
A process of extracting boundary candidate information that is expected to be a boundary of the predetermined region from the image information of the frame image using a predetermined method;
And further
The fitting process is
A process for determining the fitting function based on the boundary candidate information;
Including
The shape model change process includes:
At least one of the processes (a) to (c) performed based on the boundary candidate information;
including,
Image processing device. The image processing apparatus according to claim 4,
In the case of changing the fitting application range narrowly,
The process (a) in the shape model change process is:
Processing to reduce the predetermined number;
Including
The process (b) in the shape model change process is:
Among the predetermined number of functions, in the function that narrows the fitting application range, a process of lowering the upper limit of the allowable range of the order of the function,
including,
Image processing device. An image processing apparatus according to any one of claims 1 to 5, wherein
The shape model change process includes:
Including a process of changing the shape model based on a constraint condition storing a predetermined rule or order,
Image processing device. An image processing apparatus according to any one of claims 1 to 5, wherein
In the case where there is a predetermined area adjacent area close to the predetermined area,
A time change detecting means for detecting a time change information by detecting a periodic time change of the predetermined region adjacent region synchronized with the frame image;
Further comprising
The shape model change process includes:
Including changing the shape model based on the time change information.
Image processing device. The image processing apparatus according to claim 1,
Fitting result storage means for sequentially storing the predetermined region boundary information output from the shape model fitting means;
Further comprising
The shape model change process includes:
A first model changing process for changing from a shape model subjected to the fitting process to the frame image to a shape model used for the fitting process of the next frame image of the frame image;
A second model change process for changing from a shape model subjected to the fitting process to the frame image to a shape model used for the fitting process for the frame image again;
Including
The shape model control means includes
A shape model appropriate evaluation means for obtaining evaluation information based on a shape model sequentially changed by the shape model control means or a fitting function in predetermined area boundary information stored in the fitting result storage means;
The shape model control means includes
Based on the evaluation information, at least one of the first model change process and the second model change process is performed.
Image processing device. The image processing apparatus according to claim 8,
In the case where the fitting result storage means stores the predetermined area boundary information of 3 or more,
The evaluation information is
Including a fitting function group indicating three or more fitting functions in the predetermined area boundary information stored in the fitting result storage means;
Image processing device. The image processing apparatus according to claim 8,
The shape model control means includes
The boundary of the predetermined area approximated by the fitting function in the predetermined area boundary information and the boundary of the predetermined area extracted from the image information of the frame image used for deriving the fitting function are expected. Fitting accuracy calculating means for calculating image comparison information comparing the difference between the boundary candidate information and
Including
The evaluation information is
Including the image comparison information,
Image processing device. The image processing apparatus according to claim 8,
The shape model control means includes
Fitting history storage means generally stored as fitting history information by sequentially storing the shape models sequentially determined by the shape model control means;
Further comprising
The evaluation information is
Including the fitting history information,
Image processing device. The image processing apparatus according to claim 9,
In the case where there is a predetermined area adjacent area close to the predetermined area,
The shape model control means includes
A position shape change calculating means for calculating position shape change information indicating a temporal position displacement amount or shape change of the predetermined area adjacent area between the fitting functions in the predetermined area boundary information stored in the fitting result storage means;
Further comprising
The evaluation information is
The position shape change information is included,
Image processing device. The image processing apparatus according to claim 8,
In the case where there are a plurality of predetermined area adjacent areas close to the predetermined area,
The shape model control means includes
A positional relationship calculating means for calculating positional relationship information indicating a relative positional relationship between estimated adjacent regions of the predetermined region from which a fitting function in the predetermined region boundary information is derived;
Further comprising
The evaluation information is
Including the positional relationship information,
Image processing device. The image processing apparatus according to claim 8,
In the case where there is a predetermined area adjacent area close to the predetermined area,
A time change detecting means for detecting a time change information by detecting a periodic time change of the predetermined region adjacent region synchronized with the frame image;
Further comprising
The shape model control means includes
Prioritized change pattern storage means for preliminarily storing prioritized change pattern information created by attaching a priority corresponding to a periodic time change of the predetermined region adjacent area to the shape model change pattern;
Further comprising
The evaluation information is
It includes the change pattern information with priority associated with the time change,
Image processing device. The image processing device according to any one of claims 1 to 14,
The predetermined region includes a lung field region,
Image processing device. An image processing apparatus according to any one of claims 7, 12 to 14, wherein
The predetermined area includes a lung field area;
The predetermined area proximity area is:
Including a diaphragm region, a heart region, and an aortic region,
Image processing device.   A program for causing a computer to function as the image processing apparatus according to any one of claims 1 to 16, when executed by a computer included in the image processing apparatus.

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