JP2014079312A - Image processing apparatus and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing technique achieving an appropriate diagnosis and an improvement in diagnostic efficiency by a moving image.SOLUTION: An image processing apparatus 3 comprises: a pixel determination unit 120 that includes an object pixel determination part 121 for determining an object pixel in each object area of a plurality of frame images IG constituting a moving image; a period classification unit 130 that detects an object area period as a periodic time change of the object area and classifies a plurality of frame images IG into the object area period cycle; a period evaluation unit 140 that includes an object pixel evaluation part 240 for evaluating each object area period on the basis of a pixel value of the object pixel and acquires a periodic evaluation result of the object pixel for each object area period; and a period selection unit 150 that selects a specific object area period on the basis of the periodic evaluation result and acquires a frame image classified into the specific object area period as a specific periodic frame image.

Description

  The present invention relates to an image processing technique for a dynamic image in which a human or animal body is photographed.

  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, by applying digital technology, it is possible to relatively easily acquire a moving image (an image group composed of a plurality of frame images) that captures the motion of an affected area using X-rays or the like. Yes.

  In this case, since a dynamic image can be taken with respect to a subject area including a diagnosis target area using a semiconductor image sensor such as an FPD (flat panel detector), it has not been possible with conventional still image photography and diagnosis by X-ray photography. It has become possible to carry out diagnosis based on motion analysis of a diagnosis target region or the like. In particular, in blood flow diagnosis, it is important for diagnosis to confirm how blood flow is flowing in blood vessels in a specific area such as the periphery or not. Diagnosis is needed.

  Thus, in recent years, various image processing techniques for performing diagnosis using dynamic images have been proposed. For example, in the technique disclosed in Patent Document 1, in an X-ray computed tomography apparatus, a technique for reconstructing a tomographic image by weighted addition of projection data having the same view (rotation angle of the X-ray tube) among a plurality of heartbeat cycles. It is disclosed.

  In addition, the technique disclosed in Patent Document 2 discloses a technique for calculating the amplitude and the period as a feature amount for each expiration / heartbeat period and generating and displaying diagnosis support information based on the feature amount in the imaging dynamic system. ing.

  Furthermore, the technique disclosed in Patent Document 3 discloses a technique for measuring blood flow dynamics information from a transfer function by measuring a time concentration curve of an artery with a contrast medium at two locations in a blood flow dynamic analysis technique.

  By the way, in the X-ray dynamic imaging, since the imaging frame rate is not sufficient for the imaging capability of the X-ray imaging equipment, the exposure dose reduction, etc., the blood flow waveform is not always seen in a desired region. Diagnosis must be made after searching for a visible cycle from all (all frame images), and time and labor are required for the diagnosis. In addition, heart rate may be extremely high among patients with diseases, and blood flow information obtained by analyzing X-ray dynamic images tends to be insufficient in frame rate relative to the actual blood flow velocity of the subject. In some cases, it is difficult to observe the blood flow and heartbeat waveform in the examination target area, so the diagnosis efficiency is poor. In addition, the blood flow waveform varies in phase and amplitude depending on the position inside the body, so the optimal expression is obtained over the entire area. There is a problem that it is difficult.

JP 2012-61324 A JP 2009-273671 A JP 2011-131041 A

  On the other hand, in the prior art of the above-mentioned patent document 1, since data of different periods is forcibly weighted and added, there is a very high accuracy requirement for alignment for each period, and there is a problem that the data deteriorates.

  Moreover, in the prior art of the above-mentioned Patent Document 2, evaluation regarding normal / abnormal pulmonary function and cardiac function for each cycle is performed, but evaluation of whether or not the frame rate is insufficient for each cycle is not disclosed. For this reason, since the evaluation is performed uniformly for each period, there is a problem that the reliability of the diagnosis support information is different for each period.

  Furthermore, the prior art of Patent Document 3 also does not disclose an evaluation of whether or not the frame rate is insufficient for each period.

  The present invention has been made in view of such circumstances, and for a moving image in which a state in which a physical state of a predetermined region in a human body or an animal body periodically changes in time is sequentially captured, An object of the present invention is to provide an image processing technique that realizes appropriate diagnosis by moving images and improvement of diagnosis efficiency with respect to a state change of a predetermined region without performing complicated processing.

  In order to solve the above-mentioned problem, the invention of claim 1 is composed of a plurality of frame images in which a physical state of a predetermined region in a human body or an animal body periodically changes over time at each frame shooting period. A pixel determining unit that includes a moving image acquiring unit that acquires a moving image to be processed, and includes a predetermined pixel determining unit that determines a predetermined pixel in each predetermined region of the plurality of frame images; and a periodic time of the predetermined region Detecting a predetermined region period that changes, period classification means for classifying the plurality of frame images into units of the predetermined region period, and evaluating each predetermined region period based on a predetermined pixel value that is a pixel value of the predetermined pixel A period evaluation unit that includes a predetermined pixel evaluation unit and obtains a period evaluation result of the predetermined pixel for each of the predetermined area periods; and a specific predetermined area period is selected based on the period evaluation result And further comprising a cycle selection means for a frame classified images to the specific predetermined regions cycle as specific cycle frame image.

  The invention of claim 2 is the image processing apparatus according to claim 1, wherein the predetermined pixel evaluation unit is a predetermined pixel time indicating a density change in the time direction of the predetermined pixel value within the predetermined region period. A predetermined pixel time density change calculation unit for calculating a density change; (a1) at least one of the maximum value and the minimum value in the predetermined pixel time density change; and (a2) a maximum differential value in the predetermined pixel time density change. And at least one of the minimum differential values, (a3) a value that is an extreme value in the predetermined pixel time density change, and (a4) the predetermined pixel value in the frame image that generates the predetermined pixel time density change is (1) the number of frame images that are equal to or greater than a predetermined value; (a5) a pixel integral value obtained by integrating pixel values at a second predetermined value or more in the predetermined pixel time density change in the time direction; A predetermined pixel evaluation value for calculating a predetermined pixel evaluation value based on at least one of the determination elements (a1) to (a6) of the correlation value between a predetermined pixel time density change and a reference change model prepared in advance. And a cycle evaluation result determination unit that determines the predetermined pixel evaluation value as the cycle evaluation result.

  The invention according to claim 3 is the image processing device according to claim 2, wherein the reference change model is a space of pixel values of a plurality of pixels along a blood flow direction in a blood vessel region on the frame image. It is created by using a spatial density change indicating a density change in the direction.

  The invention according to claim 4 is the image processing device according to claim 2, wherein the pixel determining means includes a reference pixel determining unit that determines a reference pixel different from the predetermined pixel on the frame image. The predetermined pixel evaluation unit further includes a reference pixel time density change calculating unit that calculates a reference pixel time density change indicating a density change with respect to a time direction of a reference pixel value that is a pixel value of the reference pixel within the predetermined region period. (B1) at least one value among the maximum value and the minimum value in the reference pixel time density change, and (b2) at least one value among the maximum differential value and the minimum differential value in the reference pixel time density change, (B3) At least one value of a maximum value and a minimum value in the reference pixel time density change, and (b4) a frame image constituting the reference pixel time density change. Of these, the number of frame images in which the reference pixel value is equal to or greater than a third predetermined value, (b5) a pixel integral value obtained by integrating pixel values at the fourth predetermined value or more in the reference pixel time density change in the time direction, (b6) Of the determination elements (b1) to (b6) of the correlation value between the reference pixel time density change and the reference change model prepared in advance, the determination elements (a1) to (a6) used for calculating the predetermined pixel evaluation value ) And a first spatial correlation value that calculates a difference between the predetermined pixel evaluation value and the reference pixel evaluation value as a spatial correlation value. A spatial correlation value calculation unit that executes a calculation process, wherein the period evaluation result determination unit determines the validity of the period evaluation result based on the spatial correlation value.

  The invention according to claim 5 is the image processing apparatus according to claim 2, wherein the pixel determining means includes a reference pixel determining unit that determines a reference pixel different from the predetermined pixel on the frame image. The predetermined pixel evaluation unit further includes a reference pixel time density change calculating unit that calculates a reference pixel time density change indicating a density change in the time direction of the reference pixel value within the predetermined region period; and the predetermined pixel time density A second spatial correlation value for calculating a sum of differences between the predetermined pixel value and the reference pixel value obtained by comparing a periodic change of the change and a periodic change of the reference pixel time density change as a spatial correlation value A spatial correlation value calculation unit that executes a calculation process, wherein the period evaluation result determination unit determines the validity of the period evaluation result based on the spatial correlation value.

  The invention according to claim 6 is the image processing device according to claim 4 or 5, wherein the predetermined pixel evaluation unit performs (c1) the predetermined pixel time as preprocessing for obtaining the spatial correlation value. (C2) correction for matching the deviation between the phase of density change and the phase of change in reference pixel time density, and (c2) the phase change of the predetermined pixel time density and / or the phase of reference pixel time density according to the distance from the heart region A phase amplitude correction unit that performs at least one of the corrections (c1) and (c2) for adjusting the amplitude after calculating the reference pixel time density change is further included.

  The invention according to claim 7 is the image processing apparatus according to claim 4, wherein the reference pixel determining unit refers to a pixel whose spatial distance to the predetermined pixel is equal to or smaller than a predetermined distance value. It is determined as a pixel.

  The invention according to claim 8 is the image processing apparatus according to claim 4, wherein the reference pixel determination unit determines a plurality of reference candidate pixels that are candidates for the reference pixel, and the predetermined pixel evaluation unit includes: A reference candidate pixel time density change indicating a density change with respect to a time direction of a reference candidate pixel value which is a pixel value of the reference candidate pixel within the predetermined region period is calculated, and the maximum value among the reference candidate pixel time density changes is A change that satisfies a predetermined threshold or more is determined as a reference pixel time density change.

  The invention according to claim 9 is the image processing apparatus according to claim 2, wherein the predetermined pixel includes a plurality of pixels, the predetermined pixel evaluation value includes a plurality of predetermined pixel evaluation values, and the predetermined pixel. The evaluation value calculation unit calculates the plurality of predetermined pixel evaluation values for the plurality of predetermined pixels, and the period evaluation unit includes (d1) a sum of the plurality of predetermined pixel evaluation values, and (d2) the plurality of predetermined pixels. Among pixel evaluation values, the sum of the predetermined pixel evaluation values greater than or equal to a predetermined value, (d3) the highest value among the plurality of predetermined pixel evaluation values, (d4) change in the predetermined pixel time density between the plurality of predetermined pixels A statistical predetermined pixel evaluation value calculation unit that calculates a statistical predetermined pixel evaluation value based on at least one statistical value among the statistical values (d1) to (d4) of the similarity The previous statistical predetermined pixel evaluation value Including periodic evaluation result determination unit which determines a period evaluation results.

  The invention according to claim 10 is the image processing apparatus according to claim 4 or 5, wherein the predetermined pixel includes a plurality of pixels, and the predetermined pixel evaluation value includes a plurality of predetermined pixel evaluation values. The predetermined pixel evaluation value calculation unit calculates the plurality of predetermined pixel evaluation values for the plurality of predetermined pixels, and the reference pixel is different from the plurality of predetermined pixels and corresponds to a plurality of predetermined pixels. The plurality of spaces each including a reference pixel, wherein the spatial correlation value calculation unit executes the first or second spatial correlation value calculation processing on the plurality of reference pixels and corresponds to the plurality of predetermined pixel evaluation values. A correlation value is calculated, and the period evaluation means includes a predetermined number of effective predetermined pixel evaluation values or a predetermined number of effective predetermined pixels determined to be effective based on the plurality of spatial correlation values among the plurality of predetermined pixel evaluation values. (D1) A sum total of constant effective predetermined pixel evaluation values, (d2) of the predetermined number of effective predetermined pixel evaluation values, a total sum of the predetermined pixel evaluation values equal to or greater than a predetermined value, and (d3) the predetermined number of effective predetermined pixel evaluation values. A statistical value based on at least one statistical value among the statistical values (d1) to (d4) of the highest value among them, and (d4) similarity of the predetermined pixel time density change between the predetermined number of effective predetermined pixels. A statistical predetermined pixel evaluation value calculating unit that calculates a predetermined pixel evaluation value; and the cycle evaluation unit includes a cycle evaluation result determining unit that determines the statistical predetermined pixel evaluation value as the cycle evaluation result.

  The invention according to claim 11 is the image processing device according to any one of claims 1 to 10, wherein the moving image analysis is performed on the frame image including the specific period frame image, Analyzing means for obtaining the moving image analysis result is further provided.

  The invention according to claim 12 is the image processing apparatus according to claim 11, further comprising display means for displaying the specific period frame image or the moving image analysis result.

  The invention according to claim 13 is the image processing apparatus according to claim 11, further comprising storage means for storing the specific periodic frame image or the moving image analysis result.

  The invention according to claim 14 is the image processing apparatus according to claim 11, further comprising communication means for communicating the specific period frame image or the moving image analysis result.

  The invention of claim 15 is the image processing apparatus according to any one of claims 1 to 14, wherein the predetermined region includes a blood vessel region.

  The invention of claim 16 is the image processing apparatus according to claim 11, wherein the predetermined pixel determining unit sequentially changes and determines predetermined pixels while moving along a blood vessel, and the changed predetermined The image processing apparatus further includes display means for displaying the specific periodic frame image or the moving image analysis result obtained for each pixel adjacently or in a superimposed manner.

  The invention of claim 17 is the image processing device according to any one of claims 1 to 16, wherein the body moves spatially during the shooting of the moving image. Position correspondence processing means for performing a process of associating the spatial position of each pixel between the frame images immediately after acquired by the moving image acquisition means in the time direction, and outputting the processed frame image to the pixel determination means; Is further provided.

  The invention of claim 18 is a program for causing a computer to function as the image processing apparatus according to any one of claims 1 to 17 when executed by a computer included in the image processing apparatus. It is.

  According to the first to seventeenth aspects of the present invention, a predetermined region period that is a periodic time change of the predetermined region is detected, a plurality of frame images are classified into the predetermined region period units, and the predetermined region value is determined based on the predetermined pixel value. A period evaluation unit that evaluates each area period and obtains a period evaluation result, selects a specific predetermined area period based on the period evaluation result, and sets a frame image classified into the specific predetermined area period as a specific period frame image Obtaining cycle selection means. That is, by evaluating each predetermined region period based on a predetermined pixel value, it is possible to determine whether it is necessary for diagnosis or whether it is an important period. As a result, an expert such as a doctor selectively examines a specific period frame image among a plurality of frame images, thereby appropriately and efficiently performing a video diagnosis for a state change of a predetermined area within a predetermined area period. Can be performed. Further, the diagnosis time can be shortened by moving image interpretation, and convenience for the user is improved.

  According to the invention of claim 2, the predetermined pixel evaluation unit includes the predetermined pixel time density change calculation unit and the predetermined pixel evaluation value calculation unit, so that, for example, as the predetermined pixel evaluation value, the maximum in the determination element (a1) When calculating the value or the difference between the maximum value and the minimum value, the height of the waveform peak, which is the most important information of the predetermined pixel time density change, can be captured. Further, when the predetermined pixel evaluation value is calculated based on the maximum differential value, the minimum differential value, and the extreme value (maximum value and minimum value) in the determination elements (a2) and (a3), the waveform shape of the predetermined pixel time density change is changed. It becomes possible to know. Furthermore, when the predetermined pixel evaluation value is calculated based on the correlation value between the predetermined pixel time density change and the reference change model in the determination element (a6), it is possible to obtain the overall expressibility of the predetermined pixel time density change. Become.

  As described above, by determining the predetermined pixel evaluation value as the period evaluation result, it is possible to appropriately determine whether it is necessary for diagnosis or whether it is an important predetermined region period.

  According to the invention of claim 3, by creating the reference change model based on the spatial density change, it is possible to appropriately obtain the correlation value between the target pixel time density change and the reference change model.

  According to the invention of claim 4 or claim 5, the cycle evaluation result determination unit determines the validity of the cycle evaluation result based on the spatial correlation value. Thereby, the correlation between the predetermined pixel and the surrounding pixels can be seen, and it can be determined whether the predetermined pixel value is noise or a significant signal.

  According to the invention of claim 6, the predetermined pixel evaluation section further includes a phase amplitude correction section as preprocessing for obtaining a spatial correlation value. As a result, the difference in phase and amplitude due to the difference in position between the predetermined pixel and the reference pixel on the frame image is corrected, and the periodic change in the predetermined pixel time density change and the periodic change in the reference pixel time density change are in phase. It is possible to compare them as changes within. As a result, the cycle evaluation unit can accurately determine the effectiveness of the cycle evaluation result.

  According to the seventh aspect of the present invention, a pixel having a signal similar to the signal of the predetermined pixel is determined as a reference pixel by determining a pixel whose spatial distance to the predetermined pixel is equal to or smaller than a predetermined distance value as a reference pixel. It becomes possible. As a result, a spatial correlation value suitable for determining the validity of the periodic evaluation result is obtained, and appropriate evaluation can be performed.

  According to the eighth aspect of the present invention, by determining, as the reference pixel time density change, a change in which the maximum value satisfies a predetermined threshold value or more among the reference candidate pixel time density changes, the noise among the plurality of reference candidate pixels is determined. Since the reference pixel can be determined by excluding the pixels having the symbol, it is possible to obtain a spatial correlation value suitable for determining the validity of the period evaluation result, and to perform appropriate evaluation.

  According to the ninth aspect of the present invention, the period evaluation result includes the statistical predetermined pixel evaluation value, so that an appropriate period selection can be determined from a wide area on the frame image. That is, it is possible to output a periodic evaluation result based on a pixel group having the highest statistical predetermined pixel evaluation value in the predetermined region (capturing a periodic waveform feature).

  Further, the sum of the predetermined pixel evaluation values equal to or greater than the statistical value (d2), the statistical value (d3) the highest value among the predetermined pixel evaluation values, and the statistical value (d4) the change in the predetermined pixel time density between the predetermined pixels. When calculating a predetermined statistical pixel evaluation value based on at least one statistical value of similarity, a determination is made to exclude an area (pixel) having a low predetermined pixel evaluation value or a desired signal from a predetermined pixel. Can do. As a result, a region (pixel) having a low predetermined pixel evaluation value or no desired signal is removed, so that the calculation time can be shortened and noise can be stably and rapidly removed.

  According to the invention of claim 10, the effectiveness of a plurality of predetermined pixel evaluation values is determined based on a plurality of spatial correlation values. As a result, the correlation between each of the plurality of predetermined pixels and the surrounding pixels can be seen, and after determining whether the plurality of predetermined pixel values are noise or significant signals, a predetermined number of effective predetermined pixels determined to be valid Based on the pixel evaluation value or a predetermined number of effective predetermined pixels, the statistical predetermined pixel evaluation value can be accurately calculated.

  According to the invention of claim 11, if the moving image analysis is performed only on the specific periodic frame image, the analysis processing time can be shortened.

  According to the invention of claim 12, the diagnosis time can be shortened by displaying only the specific periodic frame image or only the moving image analysis result.

  On the other hand, instead of completely deleting frame images other than a specific predetermined area period, only a specific period frame image of a specific predetermined area period is displayed at a normal speed, and other frame images are displayed at high speed or thinned out. Can be done. Also, moving image analysis is performed on frame images other than a specific predetermined region period, and only moving image analysis results of a specific predetermined region period are displayed at a normal speed, and moving image analysis results of other frame images are displayed at high speed. Alternatively, thinning display can be performed.

  According to the invention of claim 13, by storing only the specific period frame image or only the moving image analysis result, the storage area can be reduced, and the storage capacity can be saved.

  According to the fourteenth aspect of the present invention, communication load can be suppressed and communication time can be shortened by communicating only a specific periodic frame image or only the moving image analysis result.

  According to the fifteenth aspect of the present invention, since the predetermined region is a blood vessel region, it is possible to select a blood flow cycle in which a blood flow amount, a blood amount and the like in a predetermined pixel of the desired blood vessel region are depicted.

  For example, it is possible to capture a rapid change in blood flow from the maximum differential value of the time concentration change and a small waveform such as a wave or v wave from the maximum value of the time concentration change. In addition, the blood volume can be evaluated by obtaining the number of frame images of the determination element (a4) and the pixel integration value of the determination element (a5).

  For this reason, an efficient medical diagnosis is possible with respect to a change in the state of blood flow within a blood flow cycle of a predetermined pixel. In addition, the diagnosis time can be shortened by moving image interpretation in the blood vessel region, and convenience for the user is improved.

  According to the invention of claim 16, the predetermined pixel determining unit sequentially changes and determines the predetermined pixels while moving along the blood vessel, and the specific periodic frame image or moving image analysis result obtained for each changed predetermined pixel Is displayed adjacently or superimposed. Thereby, even when the affected part cannot be specified, the affected part can be sequentially searched by the superimposed display, and an efficient medical diagnosis can be performed.

  According to the invention of claim 17, in the case where the body moves spatially during moving image shooting, the spatial position of each pixel between the frame images immediately after being acquired by the moving image acquisition means is associated with the time direction. And a position correspondence processing unit that outputs the processed frame image to the pixel determination unit. That is, by correcting the spatial movement of the body by corresponding point search processing or the like, it is possible to correct the positional relationship of the predetermined pixels in the time direction between the frame images. Thereby, even if the body moves spatially during imaging, accurate medical diagnosis can be performed with respect to a state change of a predetermined region within a specific predetermined region period.

  According to the invention of claim 18, the same effect as that of the invention of claims 1 to 17 can be obtained.

1 is a diagram illustrating an overall configuration of a radiation dynamic image capturing system 100 according to a first embodiment. It is a figure which illustrates the general characteristic of a blood-flow waveform. It is a figure explaining the problem in a blood-flow waveform and imaging | photography timing. 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 determination method of the object pixel in an object area | region. It is a figure explaining the blood-flow period of this object pixel at the time of classifying a plurality of frame images into the blood-flow period unit of an object pixel with a blood-flow waveform. It is a block diagram which shows the detail of the period evaluation part 140 which concerns on 1st Embodiment. It is a figure which illustrates object pixel time density change. It is a figure which illustrates object pixel time density change and a judgment element. It is a figure explaining selecting a specific cycle frame image classified into a specific blood flow cycle. 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 block diagram which shows the detail of 140 A of period evaluation parts which concern on 2nd Embodiment. It is a block diagram which shows the detail of period evaluation part 140A 'concerning 2nd Embodiment. It is a block diagram explaining the modification of the selection method of a reference pixel. It is a figure which illustrates the phase relationship of object pixel time density change and reference pixel time density change. 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 figure which illustrates the determination method of a some target pixel. It is a block diagram which shows the detail of the period evaluation part 140B which concerns on 3rd Embodiment. 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 detail of the period evaluation part 140C which concerns on 4th Embodiment. It is a block diagram which shows the detail of the period evaluation part 140C 'concerning 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 figure which illustrates a blood flow phase analysis result. It is a figure which illustrates the waveform which shows the time change of the blood-flow signal value of a pulmonary vascular region. It is a figure explaining the case where a target pixel is changed and determined sequentially, moving along a blood vessel. 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 the spatial positional relationship of each pixel between frame images when a body moves spatially during shooting of a moving image. It is a flowchart explaining the basic operation | movement of the image processing apparatus 3E implement | achieved in 6th Embodiment. It is a figure explaining the production method of the reference | standard change model NM based on a space density change.

<1. First Embodiment>
The radiation dynamic imaging system according to the first embodiment of the present invention will be described below.

<1-1. Overall Configuration of Radiation Dynamic Imaging System>
The radiation dynamic image capturing system according to the first embodiment captures a radiographic image with respect to a situation in which the physical state of a target region (predetermined region) of a subject periodically changes over time using a human or animal body as a subject. Do.

  FIG. 1 is a diagram illustrating an overall configuration of a radiation dynamic image capturing system according to the first embodiment. As shown in FIG. 1, the radiation dynamic image capturing system 100 includes an image capturing device 1, an image capturing control device 2 (imaging console), and an image processing device 3 (diagnosis console). The imaging device 1 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 device 3 are connected via a communication network NT such as a LAN (Local Area Network). Yes. Each device constituting the radiation dynamic image capturing system 100 conforms to the DICOM (Digital Image and Communications in Medicine) standard, and communication between the devices is performed in accordance with DICOM.

<1-1-1. 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 chest dynamics of the subject M accompanying breathing. Dynamic imaging is performed by acquiring a plurality of images in chronological order while repeatedly irradiating the chest of the subject M 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 M with radiation (X-rays) under the control of the radiation irradiation control device 12. The illustrated example is a system for the human body, and the subject M corresponds to the person to be inspected. Hereinafter, the subject M 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 M 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 one frame image acquisition operation to the start of the next frame image acquisition operation in continuous shooting, and coincides with the pulse interval.

  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 device 16 detects the breathing cycle of the subject M and outputs it to the control unit 21 of the imaging control device 2. In addition, the cycle detection device 16 includes a cycle detection sensor (not shown) that detects the breathing cycle of the subject M, and a timing unit (not shown) that measures the time of the breathing cycle detected by the cycle detection sensor and outputs the time to the control unit 21. (Not shown).

<1-1-2. 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.

<1-1-3. Configuration of Image Processing Device 3>
The image processing device 3 acquires the dynamic image transmitted from the imaging device 1 via the imaging control device 2 and displays an image for a doctor or the like to perform an interpretation diagnosis.

  As shown in FIG. 1, the image processing apparatus 3 includes a control unit 31, a storage unit 32, an operation unit 33, a display unit 34, a communication unit 35, and an analysis unit 36. They are connected by a bus 37.

  The control unit 31 includes a CPU, a RAM, and the like. The CPU of the control unit 31 reads the system program and various processing programs stored in the storage unit 32 in accordance with the operation of the operation unit 33, expands them in the RAM, and executes various processes according to the expanded programs. The operation of each part of the image processing apparatus 3 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 and data such as parameters necessary for execution of processing by the programs or processing results. For example, the storage unit 32 stores an image processing program for executing image processing to be described later. These various programs are stored in the form of readable program codes, and the control unit 31 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.

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

  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.

  The analysis unit 36 performs, for example, blood flow phase analysis (see FIGS. 27 and 28) described in detail later on the image data output from the reading control device 14 or image data temporarily stored in the storage unit 32. Perform such image analysis. Then, the image information generated as a result of analysis by the analysis unit 36 is output to the storage unit 32, the display unit 34, and the communication unit 35. In the image processing apparatus 3 according to the first embodiment, it is not essential to include the analysis unit 36.

<1-2. General characteristics of blood flow waveform and problems in conventional video diagnosis>
As a premise for explaining the details of the image processing apparatus 3 in this embodiment, general characteristics of the blood flow waveform and problems of the moving image diagnosis in the prior art will be described.

  FIG. 2 is a diagram illustrating a general characteristic of a blood flow waveform for one cycle, where the horizontal axis indicates the time direction, and the vertical axis indicates pixel values representing light and shade. Note that the time-series waveform of pixel values (blood flow waveform) is shown by inverting the time-series waveform of pixel values actually detected in all the drawings including FIG. For this reason, the amount of X-ray attenuation increases and the actual pixel value decreases as blood is pumped from the heart into the lung field in accordance with the heartbeat, but it increases in all the following figures. Is shown in

  As shown in FIG. 2, the blood flow waveform for one cycle is characterized by a peak B1, a peak range B2, and an integrated value of the peak range (a value obtained by integrating pixel values of a certain value v0 or more in the time direction) B3. Have These features are indexes that quantitatively represent blood flow dynamics. Peak B1 indicates blood flow (blood flow amplitude), peak range B2 indicates passage time (blood flow passage time), and integration of the peak range. The value B3 indicates the blood volume.

  FIG. 3 is a diagram showing a blood flow waveform specialized around the peak B1 and imaging timing synchronized therewith. Note that the shooting timing interval corresponds to the frame shooting period FP included in the moving image.

  For example, when an expert such as a doctor diagnoses a blood flow volume (blood flow amplitude) from a moving image, the peak B1 needs to be included as an imaging timing. However, in the case as shown in FIG. 3, since the imaging timing is not at the peak B1 of the blood flow waveform but at positions ts1 and ts2 around the peak B1, it is possible to correctly detect the blood flow (blood flow amplitude). It is not possible to perform an appropriate dynamic diagnosis. Further, it takes time and effort to find a period (frame image) in which the photographing timing satisfies a desired requirement.

  Under such circumstances, blood flow volume, blood volume, and blood flow passage are appropriately applied to a specific cycle in which parameters such as peak B1, peak range B2, and integrated value B3 of the peak range are depicted in the frame image. It is desired to perform dynamic diagnosis after detecting time.

  Therefore, in the present invention, by selecting a frame image corresponding to the specific period, it is possible to appropriately and efficiently perform a dynamic diagnosis with respect to a state change of a target region within the period.

  Below, the detail of the image processing apparatus 3 in 1st Embodiment is demonstrated.

<1-3. Specific Configuration of Image Processing Device 3>
The image processing device 3 of the radiation dynamic image capturing system 100 according to the first embodiment of the present invention can perform moving image diagnosis appropriately and efficiently by selecting a frame image for a specific period from among a plurality of frame images. It becomes possible.

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

<1-3-1. Functional configuration of image processing apparatus 3>
FIG. 4 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 the 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 pixel determination unit 120, a period classification unit 130, a period evaluation unit 140, and a period selection unit 150.

  Hereinafter, the functional configuration of the control unit 31 as illustrated in FIG. 4 will be 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 pixel determination unit 120, the period classification unit 130, the period evaluation unit 140, and the period selection unit 150 will be sequentially described with reference to FIG.

<1-3-1-1. Moving Image Acquisition Unit 110>
In the moving image acquisition unit 110, a plurality of states in which a physical state of a target region in a human or animal body photographed by the reading control device 14 of the imaging device 1 is periodically changed with time are captured for each frame photographing cycle. A moving image composed of frame images is acquired. In the present embodiment, the target region is described as a blood vessel region. In addition, the term “physical state” here is used to indicate the concentration of blood flow (the presence or absence of blood flow).

  In FIG. 4, the imaging control device 2 is interposed between the imaging device 1 and the image processing device 3, and the detection data (frame image) stored in the storage unit 22 of the imaging control device 2 passes through the communication unit 25. To the communication unit 35 of the image processing apparatus 3.

<1-3-1-2. Pixel Determination Unit 120>
The pixel determining unit 120 includes a target pixel determining unit (predetermined pixel determining unit) that determines a target pixel (predetermined pixel) in each blood vessel region (target region) of a plurality of frame images.

  The method for determining the target area and the target pixel can be broadly classified into three types: manual, automatic, and manual assist. (I) In the case of manual operation, it is assumed that the target pixel is determined for the target region designated by an expert such as a doctor. (Ii) In the case of automatic, it determines based on the position extracted as an affected part from diagnostic information. Here, the diagnosis information may be selected from medical record information obtained as a result of the current diagnosis of the patient, or a place that was an affected part in the past may be used. (Iii) In the case of manual assist, the target pixel is determined by enlarging or reducing the manually specified target area to a necessary range.

  FIG. 5 is a diagram for explaining a method of determining a target pixel in the target region, taking the automatic case as an example. As shown in FIG. 5, the diagnosis information is set as a target area TA on the frame image IG based on the target area TA diagnosed as a diseased part from the past analysis image IA. Then, the target pixel TP is determined in the target area TA.

<1-3-1-3. Period classification unit 130>
In the period classification unit 130, the blood vessel area (target area) changes periodically in time, and the blood flow period (target area period, specifically, the target pixel TP) is independent of the frame imaging period FP (see FIG. 3). And the plurality of frame images are classified for each blood flow cycle (target region cycle) of the target pixel TP. The detection of a temporal change here means detection of a temporal change in a physical state such as a blood flow concentration.

  That is, the period classification unit 130 detects the periodic time change of the blood vessel region of the subject M, that is, the blood flow phase information and frequency (cycle) information of the target region, and then the blood flow cycle of the target pixel TP. A plurality of frame images IG are classified in units. FIG. 6 is a diagram for explaining the blood flow cycle when the plurality of frame images IG are classified into blood flow cycle units using a blood flow waveform. As shown in FIG. 6, blood flow cycles PC1 to PC3 are detected based on a periodic time change TF1 of the blood vessel region of the subject M detected by a method described later, and a plurality of blood flow cycles PC1 to PC3 are detected for each blood flow cycle PC1 to PC3. The frame image IG is classified. Here, as a method for determining the blood flow periods PC1 to PC3, for example, a method of searching for and determining the rise based on the maximum value HP of the time change TF1 can be employed.

  As a method of calculating the periodic time change TF1 (phase information due to blood flow) of the blood vessel region of the subject M, there is a method of obtaining it by the time concentration change of the blood vessel region.

  As the time density change of the blood vessel region, the time density change in a pixel with a clear signal among the pixels of the target blood vessel region is used. That is, the period classification unit 130 calculates the time density change of the pixel on the plurality of frame images IG constituting the moving image and sets it as the time density change of the target region. As a method for calculating the temporal density change of the pixels, for example, by taking the difference between the frame images IG, the density change of the clear pixel of the signal in the target blood vessel region can be used as the blood flow information. In addition, a blood flow dynamic analysis method described in JP2011-131041A can be employed.

  As described above, the period classification unit 130 automatically sets the blood flow period of the target pixel TP by setting the time density change of the target region based on the time density change of the pixel in the blood vessel region captured in the moving image. It can be detected.

  On the other hand, there is also a method of detecting the periodic time change TF1 (phase information by heartbeat) of the heart and using the heartbeat cycle without using the blood flow cycle in the blood vessel region of the subject region of the subject M. As a method for extracting a heartbeat cycle, there are roughly two methods.

  The first heart cycle extraction method is a case where an electrocardiograph is used. For example, the heartbeat period can be extracted by synchronizing the photographing operation by the photographing apparatus 1 of FIG. 1 and detecting the heartbeat phase (periodic time change TF1 of the heart) of the subject M from the electrocardiograph. .

  The second heartbeat period extraction method is a method for obtaining heartbeat information by calculating the amount of motion of the heart wall using the captured image acquired by the moving image acquisition unit 110. For example, a method for detecting the outline of the heart (eg, “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, (See Issue 3, May, 1990, pp. 342-350, etc.) and the like, and by detecting the periodic time change TF1 of the heart, the heartbeat period can be extracted.

<1-3-1-4. Period Evaluation Unit 140>
The cycle evaluation unit 140 includes a target pixel evaluation unit 240 that evaluates for each blood flow cycle PC based on the target pixel value that is the pixel value of the target pixel TP, and obtains a cycle evaluation result CR of the target pixel for each blood flow cycle PC. (See FIG. 4).

  FIG. 7 is a block diagram showing details of the period evaluation unit 140 (see FIG. 4) according to the first embodiment. As illustrated in FIG. 7, the period evaluation unit 140 includes a target pixel evaluation unit 240 and a period evaluation result determination unit 340.

<1-3-1-4-1. Target Pixel Evaluation Unit 240>
The target pixel evaluation unit 240 includes a target pixel time density change calculation unit 241 and a target pixel evaluation value calculation unit 242.

<1-3-1-4-1-1. Target Pixel Time Density Change Calculation Unit 241>
The target pixel time density change calculation unit 241 calculates a target pixel time density change indicating a density change in the time direction of the target pixel value in the blood flow cycle PC. The calculation method of the target pixel time density change can be calculated by the same method as the above-described time density change for the target pixel TP.

  FIG. 8 is a diagram illustrating a change in the target pixel time density, in which the horizontal axis indicates the shooting time and the vertical axis indicates the pixel value. The dotted line waveform indicates the reference change model NM, and the dotted vertical line interval indicates the frame shooting period FP. Here, the reference change model NM is an ideal shape model calculated from medical a priori knowledge, and is created in advance. As a creation method, a method of creating by a time density change obtained from a pixel having a clear signal value in a blood vessel (target) region on the frame moving image IG can be considered as in the case of the time density change described above.

  As another method for creating the reference change model NM, there is a method for creating it based on a change in the spatial density. The spatial density change here means a density change in the spatial direction of the pixel values of a plurality of pixels along the blood flow direction in the blood vessel region on the frame image IG.

  FIG. 34 is a diagram for explaining a method of creating a reference change model NM based on a change in spatial density. FIG. 34A illustrates the positional relationship between the target area TA (target pixel TP) on the frame image IG and the vascular area RG, and FIG. 34B illustrates a plurality of the blood flow directions AR2 in the vascular area RG. 34, the left diagram of FIG. 34C illustrates the spatial density change NS generated from the plurality of pixels rg1 to rg4, and the right diagram illustrates the created reference change model NM. In addition, the horizontal axis of the left figure of FIG.34 (c) shows a spatial direction (blood flow direction AR2), and a vertical axis | shaft shows a pixel value (blood flow value). In addition, the horizontal axis of the right diagram in FIG. 34C indicates the time direction, and the vertical axis indicates the pixel value (blood flow value).

  As shown in FIG. 34, the spatial density change NS has a clear signal value on the frame image IG. For example, a plurality of pixels rg1 to rg4 along the blood flow direction in the blood vessel region RG located near the heart. Refers to changes in density in the spatial direction. Specifically, the change in density in the spatial direction is obtained by calculating the density of each of the plurality of pixels rg1 to rg4 or the difference value (blood flow value) of the density (see the left figure in FIG. 34C). Then, the estimated temporal concentration change in the target area TA (target pixel TP) is obtained by converting the vertical axis and horizontal axis magnifications of the spatial density change NS obtained along the blood flow direction AR2 under a predetermined method. Is obtained and becomes the reference change model NM (see the right figure in FIG. 34C). Specifically, it can be created using a blood flow dynamic analysis method or the like as described in JP2011-131041A.

  As shown in FIG. 8A, the target pixel TP in the target area TA on the frame image IG is determined by the target pixel determination unit 121. Then, as shown in FIG. 8B, the target pixel time density change NT is calculated for each target blood flow cycle with respect to the target pixel TP. That is, the target pixel time density change NT1 is calculated within the blood flow cycle PC1, the target pixel time density change NT2 is calculated within the blood flow cycle PC2, and the target pixel time density change NT3 is calculated within the blood flow cycle PC3. Calculated. For convenience of explanation, the blood flow periods PC1 to PC3 in FIG. 8 show only the vicinity of the peak range B2 in one whole blood flow period (see FIG. 2).

<1-3-1-4-1-2. Target Pixel Evaluation Value Calculation Unit 242>
In the target pixel evaluation value calculation unit 242, (a1) at least one value among the maximum value and the minimum value in the target pixel time density change NT, and (a2) the maximum differential value and the minimum differential value in the target pixel time density change NT. Among these, at least one value, (a3) at least one value among the maximum value and the minimum value in the target pixel time density change NT, and (a4) the target pixel value in the frame image IG that generates the target pixel time density change NT. Is the number of frame images in which is equal to or greater than the first pixel value (first predetermined value), and (a5) pixels in which the pixel values at or above the second pixel value (second predetermined value) of the target pixel time density change NT are integrated in the time direction At least one determination element among the determination elements (a1) to (a6) of the integral value and (a6) the correlation value between the target pixel time density change NT and the reference change model NM prepared in advance. Based calculates the target pixel evaluation value.

  As described above, the target pixel evaluation value can be given using only each determination element, while a combination of some determination elements among the determination elements (a1) to (a6) or a determination element It is also possible to assign the target pixel evaluation value using all of (a1) to (a6). That is, when each evaluation value et (1) to et (m) exists for m (m is a positive integer equal to or less than 6) determination elements, the target pixel evaluation value ft is the evaluation value et (1 ) To et (m), α (1) to α (m) are calculated from the following equation (1).

  FIG. 9 is a diagram illustrating the target pixel time density change NT and its determination element in FIG. 8, where the horizontal axis indicates the shooting time and the vertical axis indicates the pixel value. As shown in FIG. 9, for example, when the maximum value in the target pixel time density change NT of the determination element (a1) is calculated in the blood flow periods PC1 to PC3, the highest evaluation is performed on the blood flow period PC1 by the point E1. The value et is given. When calculating the maximum differential value in the target pixel time density change NT of the determination element (a2) within the blood flow cycle PC1 to PC3, the highest evaluation value et is given to the blood flow cycle PC1 by the differential value E2. . As described above, the determination elements (a1) and (a2) are effective, for example, when it is desired to extract the period of the largest pixel value.

  When calculating the number of frame images in which the target pixel value is equal to or greater than the first pixel value v1 among the frame images IG that generate the determination element (a4) target pixel time density change NT within the blood flow periods PC1 to PC3, Since the number of frame images in the region E4 of the blood flow cycle PC2 and the region E4 ′ of the blood flow cycle PC3 is “2”, a higher evaluation value et is given to the blood flow cycles PC2 and PC3 than the blood flow cycle PC1. Is done. In addition, when calculating a pixel integration value obtained by integrating the pixel values of the second pixel value v2 or more in the determination element (a5) target pixel time density change NT in the blood flow cycle PC1 to PC3 in the time direction, the pixel integration value E5 gives the highest evaluation value et to the blood flow cycle PC2. As described above, the determination elements (a4) and (a5) check the presence of a frame image having a certain value or more like the first pixel value v1 and the second pixel value v2, for example, noise check and signal value. This is effective when grasping the above. In FIG. 9, the first pixel value v1 and the second pixel value v2 are set to the same pixel value, but may be different pixel values.

  Further, when the local maximum value (second maximum value) in the determination element (a3) target pixel time density change NT is calculated within the blood flow cycle PC1 to PC3, the highest evaluation of the blood flow cycle PC3 based on the local maximum value of the point E3. The value et is given. Thus, the determination element (a3) is effective when, for example, it is desired to diagnose the v-wave wv (see FIG. 3).

  Further, when calculating the correlation value between the determination element (a6) target pixel time density change NT and the reference change model NM prepared in advance in the blood flow periods PC1 to PC3, the target pixel time density change NT and the reference change model NM. The highest evaluation value et is given to the blood flow cycle PC1 having the smallest difference from the above. Thus, the determination element (a6) is effective, for example, when importance is attached to the expression of the entire cycle.

<1-3-1-4-2. Period Evaluation Result Determination Unit 340>
The cycle evaluation result determination unit 340 determines the target pixel evaluation value ft calculated by the target pixel evaluation unit 240 as the cycle evaluation result CR. Therefore, in the cycle evaluation result determination unit 340, if the target pixel evaluation value ft is high, the cycle evaluation result CR can be given high, and if the target pixel evaluation value ft is low, the cycle evaluation result CR can be given low.

<1-3-1-5. Period selection unit 150>
In the cycle selection unit 150, a specific blood flow cycle is selected based on the cycle evaluation result CR, and a frame image IG classified into the specific blood flow cycle is obtained as a specific cycle frame image among the plurality of frame images IG. . And the period selection part 150 outputs the acquired specific period frame image in the memory | storage part 32, the display part 34, and the communication part 35 (refer FIG. 4).

  FIG. 10 is a diagram for describing selection of a specific cycle frame image classified into a specific blood flow cycle. As shown in FIG. 10, a frame image SG classified into a specific blood flow period SPC is selected, and the frame image SG is displayed on the display unit 34.

  Note that after the specific period frame image SG is temporarily stored in the storage unit 32, the specific period frame image SG stored in the analysis unit 36 (see FIG. 1) is analyzed, and the obtained specific period analysis image SA is displayed. You may display in the part 34. FIG.

<1-4. Basic Operation of Image Processing Device 3 (3 ′)>
FIG. 11 is a flowchart for explaining a basic operation realized in the image processing apparatus 3 (3 ′) according to the present embodiment. Since the individual functions of each unit have already been described (see FIG. 4), only the overall flow will be described here.

  As shown in FIG. 11, first, in step S <b> 1, the moving image acquisition unit 110 of the control unit 31 (31 ′) captures a moving image (a plurality of frame images IG) captured by the reading control device 14 of the imaging device 1. Obtained via the imaging control device 2.

  In step S2, the target pixel determining unit 121 of the pixel determining unit 120 determines the target pixel TP in each target area TA of the plurality of frame images IG (see FIG. 5).

  In step S3, the period classification unit 130 (130 ') detects the blood flow period PC of the target pixel, and classifies the plurality of frame images IG into units of the blood flow period PC (see FIG. 6). Specifically, the periodic time change detection method is detected with respect to the period classification unit 130 based on the time density change analysis of the pixels of the frame image IG in the moving image.

  In step S4, the target pixel evaluation unit 240 of the cycle evaluation unit 140 evaluates for each blood flow cycle PC based on the target pixel value that is the pixel value of the target pixel TP in each of the plurality of frame images IG, thereby performing the target pixel evaluation value. ft is calculated, and the cycle evaluation result determination unit 340 determines the target pixel evaluation value ft as the cycle evaluation result CR (see FIGS. 7 to 9).

  In step S5, the cycle selection unit 150 selects a specific blood flow cycle based on the cycle evaluation result CR, and obtains a frame image classified into the specific blood flow cycle as a specific cycle frame image SG (see FIG. 10). ).

  Finally, in step S6, the cycle selection unit 150 outputs the specific cycle frame image SG selected in step S5 in the storage unit 32, the display unit 34, and the communication unit 35 (see FIG. 4), and this operation flow ends. Is done.

  As described above, in the image processing device 3 (3 ′), a periodic time change of a target blood vessel region is detected, the blood flow cycle PC independent of the frame imaging cycle FP is detected, and a plurality of frame images IG are detected as the blood flow. Classify into cycle PC units, evaluate for each blood flow cycle PC based on the target pixel value, and select a specific blood flow cycle SPC based on the cycle evaluation result CR; A cycle selection unit 150 that obtains a frame image classified into the specific blood flow cycle SPC as a specific cycle frame image SG. That is, by evaluating each blood flow period PC based on the target pixel value, it is possible to determine whether it is necessary for diagnosis or whether it is an important period. As a result, an expert such as a doctor appropriately examines a specific period frame image SG among a plurality of frame images IG, thereby appropriately performing a moving picture diagnosis for a state change of a target region in the blood flow period PC. And it becomes possible to carry out efficiently. Further, the diagnosis time can be shortened by moving image interpretation, and convenience for the user is improved.

  In addition, the target pixel evaluation unit 121 includes the target pixel time density change calculation unit 241 and the target pixel evaluation value calculation unit 242, so that, for example, as the target pixel evaluation value ft, the maximum value or the maximum value in the determination element (a1). When calculating the difference between the value and the minimum value, the height of the waveform peak, which is the most important information of the target pixel time density change NT, can be captured. In addition, when calculating the target pixel evaluation value ft based on the maximum differential value, minimum differential value, maximum value, and minimum value in the determination elements (a2) and (a3), the shape of the waveform of the target pixel time density change NT is known. Is possible. Further, when the target pixel evaluation value ft is calculated based on the correlation value between the target pixel time density change NT and the reference change model NM in the determination element (a6), the overall expressibility of the target pixel time density change NT is obtained. It becomes possible.

  Thus, by determining the target pixel evaluation value ft as the cycle evaluation result CR, it is possible to appropriately determine whether it is necessary for diagnosis or whether it is an important blood flow cycle.

  Also, by creating the reference change model NM based on the spatial density change NS, it is possible to appropriately obtain the correlation value between the target pixel time density change NT and the reference change model NM.

  Moreover, the diagnosis time can be shortened by displaying only the specific period frame image SG. On the other hand, the frame image IG other than the specific blood flow cycle SPC is not completely deleted, but only the specific cycle frame image SG of the specific blood flow cycle SPC is displayed at a normal speed, and the other frame images IG are high-speed. Display or thinning display can be performed.

  Further, by storing only the specific periodic frame image SG, the storage area can be reduced, and the storage capacity can be saved.

  On the other hand, only the frame image SG of a specific blood flow cycle SPC can be reversibly (non-) compressed, and the frame images SG other than the specific blood flow cycle SPC can be subjected to irreversible compression storage.

  In addition, when degradation of the image information of the frame image IG is not desirable, the desired display can be efficiently performed during reproduction display by storing the shooting time (or frame number) of the specific periodic frame image IG in addition to the moving image. Can be performed automatically.

  Further, by communicating only the specific periodic frame image SG, the communication load can be suppressed and the communication time can be shortened.

  On the other hand, it is possible to perform lossless (non-) compression transmission only for the frame image SG of a specific blood flow cycle SPC, and to perform lossy compression transmission for frame images SG other than the specific blood flow cycle SPC.

  Furthermore, since the target area TA is a blood vessel area, it is possible to select a blood flow cycle PC depicting a blood flow volume, a blood volume, etc. in a desired blood vessel area.

  For example, a rapid change in blood flow from the maximum differential value of time concentration change, a wave wa (a magnitude corresponding to the second maximum value), v wave wv (a magnitude of 3rd from the maximum value of the time density change) It is possible to capture a small waveform such as (see FIG. 3). In addition, the blood volume can be evaluated by obtaining the number of frame images of the determination element (a4) and the pixel integration value of the determination element (a5).

  For this reason, an efficient medical diagnosis is possible with respect to the state change of the blood vessel region TA in the blood flow cycle PC. Further, the diagnosis time can be shortened by moving image interpretation in the blood vessel region TA, and convenience for the user is improved.

<2. Second Embodiment>
FIG. 12 is a diagram illustrating a functional configuration of the control unit 31A used in the image processing apparatus 3A 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. 4) in the image processing apparatus 3 of the first embodiment. The difference from the first embodiment is that a pixel determination unit 120A corresponding to the pixel determination unit 120 of the first embodiment further includes a reference pixel determination unit 122, and the period evaluation unit 140 of the first embodiment includes a period evaluation unit 140A ( Or it is a point changed to period evaluation part 140A '). The remaining configuration is the same as that of the image processing apparatus 3.

<2-1. Pixel Determination Unit 120A>
In addition to the functional configuration of the target pixel determination unit 121, the pixel determination unit 120A further includes a reference pixel determination unit 122 that determines a reference pixel RP (see FIG. 16 described later) different from the target pixel TP on the frame image IG.

  The reference pixel determination unit 122 determines a pixel whose spatial distance from the target pixel TP is equal to or less than a predetermined distance value as the reference pixel RP.

<2-2. Period Evaluation Unit 140A>
FIG. 13 is a block diagram illustrating details of the period evaluation unit 140A according to the second embodiment. As illustrated in FIG. 13, the period evaluation unit 140A includes a target pixel evaluation unit 240A and a period evaluation result determination unit 340A.

<2-2-1. Target Pixel Evaluation Unit 240A>
In the target pixel evaluation unit 240A, in addition to the functional configuration of the target pixel time density change calculation unit 241 and the target pixel evaluation value calculation unit 242, which are the same as those of the target pixel evaluation unit 240, the reference pixel time density change calculation unit 243, and the phase amplitude A correction unit 244, a reference pixel evaluation value calculation unit 245, and a spatial correlation value calculation unit 246 are further provided.

<2-2-1. Reference Pixel Time Density Change Calculation Unit 243>
The reference pixel time density change calculation unit 243 calculates a reference pixel time density change NR indicating a density change in the time direction of the reference pixel value that is a pixel value of the reference pixel RP in the blood flow cycle PC. That is, in each blood flow cycle PC, the reference pixel time density change NR is calculated also in the reference pixel RP as in the target pixel time density change NT shown in FIG. 8 (see FIG. 16 described later).

<2-2-1-2. Phase Amplitude Correction Unit 244>
FIG. 16 is a diagram illustrating the phase and amplitude relationship between the target pixel time density change NT and the reference pixel time density change NR. FIG. 16A shows the target pixel TP and the reference pixel RP on the frame image IG. FIG. 16B shows the target pixel time density change NT with respect to the target pixel TP, and FIG. 16C shows the reference pixel time density change NR with respect to the reference pixel RP. As shown in FIGS. 16B and 16C, it can be seen that the phase and amplitude of the reference pixel time density change NR do not coincide with the phase and amplitude of the target pixel time density change NT.

  This will be specifically described with reference to FIG. 6 again. As shown in FIG. 6, when the blood flow is measured at three different positions inside the body, the blood flow waveform is measured as periodic time changes TF1, TF2, and TF3. When the distance from the heart is long in the order of time changes TF1, TF2, and TF3, the time change TF1 has the largest amplitude peak B1 (see FIG. 2) and decreases in the order of time changes TF2 and TF3. Conversely, the peak range B2 (see FIG. 2) is the smallest in the time change TF1, and increases in the order of the time changes TF2 and TF3.

  Thus, since the blood flow waveform has different phases and amplitudes depending on the position inside the body (generally, the distance from the heart), in the frame image IG photographed at the same time, simply the target pixel TP and the reference pixel This means that the pixel value cannot be compared with RP.

  Therefore, in the phase amplitude correction unit 244, (c1) correction for matching the difference between the phase of the target pixel time density change NT and the phase of the reference pixel time density change NR as preprocessing to obtain a spatial correlation value described later, (c2 ) Refer to at least one of corrections (c1) and (c2) of corrections that adjust the phase and amplitude of the target pixel time density change NT and / or the reference pixel time density change NR according to the distance from the heart This is performed after the calculation of the pixel time density change NR.

  In the case of FIG. 16, the correction (c2) is performed on the reference pixel time density change NR. That is, the reference pixel time density change NR is moved in the direction of the arrow AR (the phase is shifted), and each pixel value is corrected for attenuation to adjust the amplitude. Thus, by matching the phase and amplitude of the reference pixel time density change NR with the phase and amplitude of the target pixel time density change NT, a new reference pixel time density change is made so that it can be compared with the target pixel time density change NT. NR ′ is generated.

  In FIG. 16, the reference pixel time density change NR ′ is generated by correcting only for the reference pixel time density change NR. However, the reference pixel time density change NT ′ is corrected only for the target pixel time density change NT. It may be generated. Alternatively, the target pixel time density change NT ′ and the reference pixel time density change NR ′ may be generated by correcting both the target pixel time density change NT and the reference pixel time density change NR.

  Thus, the target pixel time density change NT (NT ′) subjected to the above correction by the phase amplitude correction unit 244 is output to the target pixel evaluation value calculation unit 242, and the reference pixel time density change NR (NR ′). Is output to the reference pixel evaluation value calculation unit 245.

<2-2-1-3. Reference Pixel Evaluation Value Calculation Unit 245>
In the reference pixel evaluation value calculation unit 245, (b1) at least one of the maximum value and the minimum value in the reference pixel time density change NR (NR ′), and (b2) the reference pixel time density change NR (NR ′). At least one value among the maximum differential value and the minimum differential value, (b3) at least one value among the maximum value and the minimum value in the reference pixel time density change NR (NR ′), and (b4) the reference pixel time density change. The number of frame images in which the reference pixel value is greater than or equal to the third predetermined value in the frame image IG constituting NR (NR ′), and (b5) pixels in the reference pixel time density change NR (NR ′) that are greater than or equal to the fourth predetermined value Among the determination elements (b1) to (b6), the pixel integration value obtained by integrating the values in the time direction, and (b6) the correlation value between the reference pixel time density change NR (NR ′) and the reference change model NM prepared in advance. Calculated as the reference pixel evaluation value fr based on the determination element corresponding to determination factor used for calculating the elephant pixel evaluation value ft (a1) ~ (a6).

  That is, the reference pixel evaluation value fr can be given using only each of the determination elements as well as the target pixel evaluation value ft, and some of the determination elements (b1) to (b6) It is also possible to assign the reference pixel evaluation value fr using a combination of the above or all of the determination elements (b1) to (b6). Therefore, the reference pixel evaluation value fr is the same as the target pixel evaluation value ft. When the evaluation values er (1) to er (m) exist for m determination elements, the target pixel evaluation value fr is When the coefficients for the evaluation values er (1) to er (m) are α (1) to α (m), they are calculated from the following equation (2).

  The evaluation elements er (1) to er (m) of the reference pixel RP are the evaluation values et (1) to et (m) of the target pixel TP calculated by the target pixel evaluation value calculation unit 242. This corresponds to the judgment element. That is, for example, when the target pixel evaluation value ft is calculated from the determination elements (a1) and (a4), the reference pixel evaluation value fr is also calculated from the determination elements (b1) and (b4), and the target pixel evaluation is performed. When the value ft is calculated using all the determination elements (a1) to (a6), the reference pixel evaluation value fr is also calculated using all the determination elements (b1) to (b6).

  In the case of FIG. 16, the target pixel time density change NT output from the phase amplitude correction unit 244 is compared with the reference pixel time density change NR ′. Here, the maximum value (point E1) and the maximum value (point E3) of each of the time density changes NT and NR 'are used as determination elements. Therefore, the target pixel evaluation value ft is obtained from the determination elements (a1) and (a3), and the reference pixel evaluation value fr is obtained from the determination elements (b1) and (b3).

<2-2-1-4. Spatial correlation value calculation unit 246>
The spatial correlation value calculation unit 246 executes a first spatial correlation value calculation process that calculates the difference between the target pixel evaluation value ft and the reference pixel evaluation value fr as the spatial correlation value fs. That is, the spatial correlation value fs is calculated by fs = | ft−fr |.

<2-2-2. Period Evaluation Result Determination Unit 340A>
In the period evaluation result determination unit 340A, the target pixel evaluation value ft of the target pixel TP is set as the period evaluation result CR, and the validity of the period evaluation result CR is determined based on the spatial correlation value fs (the presence or absence of noise is determined). (See FIG. 13). That is, as the spatial correlation value fs is smaller, the pixel value of the target pixel TP is more similar to the pixel value of the reference pixel RP (the correlation with the surrounding pixels is better), and the value of the spatial correlation value fs is smaller. If it is larger, it indicates that the pixel value of the target pixel TP is different from the pixel value of the reference pixel RP (correlation with surrounding pixels is poor). For this reason, by calculating the spatial correlation value fs, it is possible to efficiently determine whether or not the target pixel TP determined by the target pixel determination unit 121 includes noise.

  Therefore, in the cycle evaluation result determination unit 340A, if the target pixel evaluation value ft is high and the spatial correlation value fs is small (if the correlation is good), the cycle evaluation result CR is given high, and even if the target pixel evaluation value ft is high, the space is evaluated. If the correlation value fs is large (if the correlation is bad), the period evaluation result CR can be given low.

<2-3. Period Evaluation Unit 140A ′>
The cycle evaluation unit 140A ′ determines the validity of the cycle evaluation result CR by calculating the spatial correlation value in the same manner as the cycle evaluation unit 140A, but the calculation method of the spatial correlation value is different.

  FIG. 14 is a block diagram illustrating details of the period evaluation unit 140A ′ according to the second embodiment. As illustrated in FIG. 14, the period evaluation unit 140A ′ includes a target pixel evaluation unit 240A ′ and a period evaluation result determination unit 340A ′. The period evaluation unit 140A 'differs from the period evaluation unit 140A in that the spatial correlation value fs' is directly calculated without using the target pixel evaluation value ft and the reference pixel evaluation value fr.

<2-3-1. Spatial correlation value calculation unit 246 ′ of target pixel evaluation unit 240A ′>
In the spatial correlation value calculation unit 246 ′ in the target pixel evaluation unit 240A ′, the target pixel value obtained by comparing the periodic change of the target pixel time density change NT and the periodic change of the reference pixel time density change RT with the target pixel value A second spatial correlation value calculation process is performed to calculate the sum of differences from the reference pixel value as the spatial correlation value fs ′ (see FIG. 14). Here, the periodic change of the target pixel time density change NT and the periodic change of the reference pixel time density change RT are the target pixel time density change NT (NT ′) and the reference pixel time density output from the phase amplitude correction unit 244. It refers to the change NR (NR ′).

  That is, when there are L target pixels TP and reference pixels RP (L is a positive integer) to be compared in the blood flow cycle PC, the spatial correlation value fs ′ is the value of each target pixel for the target pixel TP. Using Tv (1) to Tv (L) and the respective reference pixel values Rv (1) to Rv (L) for the reference pixel RP, the calculation is performed by the following equation (3).

  That is, in the second spatial correlation value calculation process in the case of FIG. 16, the target pixel values Tv (1) to Tv (7) and the reference pixel time density for the seven target pixels TP of the target pixel time density change NT. The sum of the differences between the reference pixel values Rv (1) to Rv (7) for the seven reference pixels RP of the change NR ′ is calculated as the spatial correlation value fs ′.

<2-3-2. Period evaluation result determination unit 340A ′>
The period evaluation result determination unit 340A ′ uses the target pixel evaluation value ft of the target pixel TP as the period evaluation result CR, and determines the validity of the period evaluation result CR based on the spatial correlation value fs ′ (determines the presence or absence of noise). (See FIG. 14). That is, as the spatial correlation value fs ′ is smaller, the pixel value of the target pixel TP is more similar to the pixel value of the reference pixel RP (correlation with surrounding pixels is better), and the spatial correlation value fs ′ A large value indicates that the pixel value of the target pixel TP is different from the pixel value of the reference pixel RP (correlation with surrounding pixels is poor). Therefore, by calculating the spatial correlation value fs ′, it is possible to efficiently determine whether or not the target pixel TP determined by the target pixel determination unit 121 includes noise.

  Therefore, in the cycle evaluation result determination unit 340A ′, if the target pixel evaluation value ft is high and the spatial correlation value fs ′ is small (if the correlation is good), the cycle evaluation result CR is given high, and the target pixel evaluation value ft is high. If the spatial correlation value fs ′ is large (if the correlation is bad), the period evaluation result CR can be given low.

<2-4. Basic Operation of Image Processing Device 3A (3A ′)>
Next, FIG. 17 is a diagram illustrating an operation flow of the image processing apparatus 3A (3A ′) according to the second embodiment. In FIG. 17, steps SA1, SA3, and SA5 are the same as steps S1, S3, and S5 in FIG.

  In the second embodiment, the reference pixel determination unit 122 that did not exist in the first embodiment is added, and the cycle evaluation unit 140 is replaced with the cycle evaluation unit 140A (140A ′), so that only the following steps are performed. Be changed.

  That is, as a process similar to the first embodiment, after step SA1, as shown in FIG. 17, in step SA2, the pixel determination unit 121 causes the target pixel determination unit 121 to display each of the plurality of frame images IG. The target pixel TP in the target area TA is determined, and the reference pixel determination unit 122 determines a reference pixel RP to be compared with the target pixel TP.

  In step SA4, the target pixel evaluation unit 240A (240A ′) of the cycle evaluation unit 140A (140A ′) calculates the target pixel evaluation value ft and the spatial correlation value fs (fs ′) for each blood flow cycle PC. The period evaluation result determination unit 340A sets the target pixel evaluation value ft as the period evaluation result CR and determines the validity of the period evaluation result CR based on the spatial correlation value fs (fs ′) (FIGS. 13 and 14). , FIG. 16). The remaining steps are the same as in the first embodiment.

  As described above, in the image processing device 3A (3A ′) according to the second embodiment, the period evaluation result determination unit 340A (340A ′) determines the validity of the period evaluation result CR based on the spatial correlation value fs (fs ′). to decide. Thereby, the correlation between the target pixel TP and the surrounding pixels can be seen, and it is possible to determine whether the target pixel value is noise or a significant signal.

  The target pixel evaluation unit 240A (240A ′) further includes a phase amplitude correction unit 244 as preprocessing for obtaining the spatial correlation value fs (fs ′). Thereby, the difference in phase and amplitude due to the difference in position between the target pixel TP and the reference pixel RP on the frame image IG is corrected, and the periodic change of the target pixel time density change NT and the periodic change of the reference pixel time density change NR are corrected. The change can be compared as a change in the same phase. As a result, the cycle evaluation unit 140A (140A ′) can accurately determine the effectiveness of the cycle evaluation result CR.

  In addition, by determining a pixel whose spatial distance from the target pixel TP is equal to or smaller than a predetermined distance value as the reference pixel RP, a pixel having a signal similar to the signal of the target pixel TP can be determined as the reference pixel RP. It becomes. As a result, a spatial correlation value suitable for determining the validity of the period evaluation result CR can be obtained, and appropriate evaluation can be performed.

<2-5. Modified example of selection method of reference pixel RP>
In the above description, the reference pixel RP is determined (selected) from the spatial distance from the target pixel TP as a method for selecting the reference pixel RP. However, the following method is adopted as a modification of the method for selecting the reference pixel RP. You can also That is, the reference pixel determining unit 122 determines a plurality of reference candidate pixels that are candidates for the reference pixel RP, and the target pixel evaluating unit 240A (240A ′) is a reference candidate that is the pixel value of the reference candidate pixel in the blood flow cycle PC. A reference candidate pixel time density change indicating a density change in the time direction of the pixel value is calculated, and one change in which the maximum value satisfies a predetermined threshold or more among the reference candidate pixel time density changes is defined as a reference pixel time density change NR. You may decide.

  FIG. 15 is a block diagram illustrating a modification of the method for selecting the reference pixel RP. As shown in FIG. 15, the reference pixel time density change calculation unit 243 ″ indicates the reference candidate pixel time density indicating the density change in the time direction of the reference candidate pixel value that is the pixel value of the reference candidate pixel in the blood flow cycle PC. The change is calculated, and the reference pixel determination unit 247 determines, as the reference pixel time density change NR, one change in which the maximum value satisfies a predetermined threshold value or more among the reference candidate pixel time density changes.

  As described above, by determining, as the reference pixel time density change NR, a change in which the maximum value among the reference candidate pixel time density changes satisfies a predetermined threshold or more, a pixel having noise among a plurality of reference candidate pixels is determined. Since the reference pixel RP can be determined by excluding it, a spatial correlation value fs (fs ′) suitable for determining the validity of the period evaluation result CR can be obtained, and appropriate evaluation can be performed.

<3. Third Embodiment>
FIG. 18 is a diagram illustrating a functional configuration of the control unit 31B used in the image processing apparatus 3B configured as the third embodiment of the present invention. The control unit 31B is used as an alternative to the control unit 31 (see FIG. 4) in the image processing apparatus 3 of the first embodiment. The difference from the first embodiment is that the pixel determining unit 120 is changed to the pixel determining unit 120B, and the cycle evaluating unit 140 is changed to the cycle evaluating unit 140B. The remaining configuration is the same as that of the image processing apparatus 3.

<3-1. Pixel Determination Unit 120B>
In the target pixel determination unit 121B in the pixel determination unit 120B, the target pixel TP to be determined is a plurality of pixels. When the target pixel TP is a plurality of pixels, all the pixels in the target area TA may be set as the target pixel TP, but a part of the target area TA is used as the target pixel TP for more stable and high-speed processing. Can be considered.

  FIG. 19 is a diagram illustrating a method for determining a plurality of target pixels TP. FIG. 19A shows the target area TA on the frame image IG, and FIGS. 19B and 19C show the target area. It is a figure explaining the selection method of several target pixel TP in TA. As shown in FIG. 19 (b), the region EX that is not a blood vessel in the target region TA is excluded because the pixel variation is small, and pixels TPa to TPd having pixel variations of a certain value or more are selected as the target pixel TP. In addition, pixel variation due to respiration unrelated to blood flow may be excluded by a high-pass filter. On the other hand, as shown in FIG. 19C, a plurality of pixels Tpe to TPg may be selected as the target pixel TP along the direction perpendicular to the blood flow direction. As described above, since adjustment of the phase and amplitude is not necessary, it is effective from the viewpoint of mutual similarity of the time density change NT.

<3-2. Period Evaluation Unit 140B>
FIG. 20 is a block diagram illustrating details of the period evaluation unit 140B according to the third embodiment. As shown in FIG. 20, the cycle evaluation unit 140B includes a target pixel evaluation unit 240B and a cycle evaluation result determination unit 340B.

<3-2-1. Target Pixel Evaluation Unit 240B>
The target pixel evaluation unit 240B includes a target pixel time density calculation unit 241B and a target pixel evaluation value calculation unit 242B.

  The target pixel evaluation value calculation unit 242B calculates a plurality of target pixel evaluation values ft for the plurality of target pixels TP determined by the target pixel determination unit 121B.

  Specifically, as illustrated in FIG. 20, the target pixel time density change calculation unit 241B performs a plurality of target pixels TP (1) to TP (n) (n is a positive value) determined by the target pixel determination unit 121B. The target pixel time density change NT (1) to NT (n) is calculated for each (integer). Then, the target pixel evaluation value calculation unit 242B performs the same calculation processing as the target pixel evaluation value calculation unit 242 of the first and second embodiments, so that the target pixel time density change NT (1) to NT (n ), The target pixel evaluation values ft (1) to ft (n) are calculated.

<3-2-2. Period Evaluation Result Determination Unit 340B>
The period evaluation result determination unit 340B includes a statistical object pixel evaluation value calculation unit 341B (see FIG. 20). The statistical target pixel evaluation value calculation unit 341B calculates a statistical target pixel evaluation value St based on at least one statistical value Ft among the following statistical values (d1) to (d4).

  (D1) The statistical value Ft is calculated based on the sum of the plurality of target pixel evaluation values ft (1) to ft (n). That is, the statistical value Ft is calculated by the following equation (4), where β (1) to β (n) are coefficients for the target pixel evaluation values ft (1) to ft (n).

  (D2) The statistical value Ft is calculated based on the sum of the target pixel evaluation values ft that are equal to or greater than a predetermined value (predetermined value) among the plurality of target pixel evaluation values ft (1) to ft (n). Here, if the constant value is X, it is calculated from the following equation (5) as in the above equation (4).

  (D3) The statistical value Ft is calculated based on the highest value among the plurality of target pixel evaluation values ft (1) to ft (n). That is, it is calculated from the following equation (6).

  (D4) The statistical value Ft is calculated based on the similarity of the target pixel time density changes NT (1) to NT (n) between the plurality of target pixels TP (1) to TP (n). Here, as the similarity, a method similar to the above-described spatial correlation value fs (fs ′) (see FIG. 16) can be used. That is, in the case of FIG. 16, the spatial similarity between the target pixel time density change NT of one target pixel TP and the reference pixel time density change NR of one reference pixel RP is represented by the spatial correlation value fs (fs ′ In this statistical value (d4), the spatial similarity between the plurality of target pixel time density changes NT of the plurality of target pixels TP is calculated from the spatial correlation value.

  In addition to the above (d1) to (d4), the statistical value is based on the similarity obtained by comparing the evaluation values et of the determination elements (a1) to (a6) among the plurality of target pixels TP (1) to TP (n). Ft may be calculated.

  As described above, the statistical object pixel evaluation value St can be given using only each statistical value Ft, while a combination of several statistical values Ft among the statistical values (d1) to (d4), Alternatively, the statistical object pixel evaluation value St can be assigned using all of the statistical values (d1) to (d4). That is, when there are m2 (m2 is a positive integer of 4 or less) statistical values Ft (1) to Ft (m2), the statistical target pixel evaluation value St is the statistical value Ft (1) to Ft (m2). Assuming that the coefficient with respect to is α2 (1) to α2 (m2), it is calculated from the following equation (7).

  Finally, the cycle evaluation result determination unit 340B (cycle evaluation unit 140B) determines the statistical object pixel evaluation value St as the cycle evaluation result CR.

<3-3. Basic Operation of Image Processing Device 3B>
Subsequently, FIG. 21 is a diagram illustrating an operation flow of the image processing apparatus 3B according to the third embodiment. In FIG. 21, steps SB1, SB3, and SB5 are the same as steps S1, S3, and S5 of FIG.

  In the third embodiment, unlike the first embodiment, only the following steps are changed by determining a plurality of target pixels TP.

  That is, as a process similar to that of the first embodiment, after step SB1, as shown in FIG. 21, in step SB2, the target pixel determination unit 121B in each of the plurality of frame images IG in the pixel determination unit 120B. In the target area TA, a plurality of target pixels TP are determined (see FIG. 19).

  Further, in step SB4, the target pixel evaluation unit 240B of the cycle evaluation unit 140B performs a plurality of target pixels TP (1) to TP (n) (n is a positive integer) for each blood flow cycle PC. The target pixel evaluation values ft (1) to ft (n) are calculated, and then the statistical target pixel evaluation value calculation unit 341B of the cycle evaluation result determination unit 340B performs the target pixel evaluation values ft (1) to ft (n). The statistical object pixel evaluation value St is calculated based on the statistical value Ft calculated by using (corresponding to at least one statistical value Ft among the above statistical values (d1) to (d4)) (see FIG. 20). . The remaining steps are the same as in the first embodiment.

  As described above, in the image processing device 3B according to the third embodiment, the cycle evaluation result CR is the statistical object pixel evaluation value St, so that it is possible to determine appropriate cycle selection from a wide area on the frame image IG. it can. That is, it is possible to output the periodic evaluation result CR based on the pixel group having the highest statistical target pixel evaluation value St (capturing periodic waveform features) in the target area TA.

  Also, the sum of the target pixel evaluation values ft of the statistical value (d2) above a certain value (predetermined value) X, the statistical value (d3) the highest value among the target pixel evaluation values ft, and a plurality of statistical values (d4) The statistical predetermined pixel evaluation value St is calculated based on at least one statistical value Ft among the similarities of the target pixel time density changes NT (1) to NT (n) between the pixels TP (1) to TP (n). In this case, it is possible to determine that a region (pixel) having a low target pixel evaluation value ft or no desired signal is excluded from the target pixel. As a result, a region (pixel) where the target pixel evaluation value ft is low or a desired signal does not exist is removed, so that the calculation time can be shortened and noise can be stably and rapidly removed.

<4. Fourth Embodiment>
FIG. 22 is a diagram illustrating a functional configuration of a control unit 31C used in the image processing apparatus 3C configured as the fourth embodiment of the present invention. The control unit 31C (31C ′) is used as an alternative to the control unit 31A (31A ′) (see FIG. 4) in the image processing apparatus 3A (3A ′) of the second embodiment. The difference from the second embodiment is that the pixel determining unit 120A is changed to the pixel determining unit 120C, and the cycle evaluation unit 140A (140A ′) is changed to the cycle evaluation unit 140C (140C ′). The remaining configuration is the same as that of the image processing apparatus 3A (3A ′).

<4-1. Pixel Determination Unit 120C>
The pixel determining unit 120C includes a target pixel determining unit 121C and a reference pixel determining unit 122C.

  In the target pixel determining unit 121C, the target pixel TP to be determined is a plurality of pixels as in the target pixel determining unit 121B. The method for selecting the plurality of target pixels TP (1) to TP (n) (n is an integer) is as described in FIG.

  Similar to the reference pixel determination unit 122, the reference pixel determination unit 122C determines reference pixels RP (1) to RP (n) corresponding to the plurality of target pixels TP (1) to TP (n), respectively. The relationship between each target pixel TP and the reference pixel RP is as described above. That is, unlike the plurality of target pixels TP (1) to TP (n), the plurality of reference pixels RP (1) to RP (n) corresponding to the plurality of target pixels TP (1) to TP (n) are determined. To do.

<4-2. Period evaluation unit 140C, period evaluation unit 140C ′>
23 and 24 are block diagrams showing details of the period evaluation units 140C and 140C ′ according to the fourth embodiment, respectively. As shown in FIG. 23, the cycle evaluation unit 140C includes a target pixel evaluation unit 240C and a cycle evaluation result determination unit 340C. As shown in FIG. 24, the cycle evaluation unit 140C ′ includes the target pixel evaluation unit. 240C ′ and a period evaluation result determination unit 340C ′.

<4-2-1. Target Pixel Evaluation Unit 240C>
The target pixel evaluation unit 240C includes a functional configuration similar to that of the target pixel evaluation unit 240A (see FIG. 13), but is different from the target pixel evaluation unit 240A in the plurality of target pixels TP (1) to TP (n). On the other hand, target pixel evaluation values ft (1) to ft (n) and spatial correlation values fs (1) to fs (n) are calculated (see FIG. 23).

  Specifically, as illustrated in FIG. 23, the target pixel time density change calculation unit 241C performs each of the plurality of target pixels TP (1) to TP (n) determined by the target pixel determination unit 121C. While calculating the target pixel time density changes NT (1) to NT (n), the reference pixel time density change calculating unit 243C has a plurality of reference pixels RP (1) to RP (n) determined by the reference pixel determining unit 122C. ), Reference pixel time density changes NR (1) to NR (n) are calculated.

  Subsequently, the phase amplitude correction unit 244C performs the same calculation processing as the phase amplitude correction unit 244 of the second embodiment on the target pixel time density change NT (1) to NT (n) and the reference pixel time density change NR (1). To n pairs with ~ NR (n). Then, the target pixel evaluation value calculation unit 242C performs the same calculation process as the target pixel evaluation value calculation unit 242 of the second embodiment, so that the target pixel time density change NT (1) to NT (n) (or , NT ′ (1) to NT ′ (n)), the target pixel evaluation values ft (1) to ft (n) are calculated respectively, and the reference pixel evaluation value calculation unit 245C of the second embodiment By performing the same calculation process as that of the reference pixel evaluation value calculation unit 245, the reference pixel time density changes NR (1) to NR (n) (or NR ′ (1) to NR ′ (n)) are performed. , Reference pixel evaluation values fr (1) to fr (n) are calculated.

  Furthermore, the spatial correlation value calculation unit 246C executes a first spatial correlation value calculation process to perform a plurality of spatial correlation values fs (1) to fs (1) to ft (n) corresponding to the plurality of target pixel evaluation values ft (1) to ft (n). fs (n) is calculated.

<4-2-2. Target Pixel Evaluation Unit 240C ′>
The target pixel evaluation unit 240C ′ has a functional configuration similar to that of the target pixel evaluation unit 240A ′ (see FIG. 14), but is different from the target pixel evaluation unit 240A ′ in that a plurality of target pixels TP (1) to TP The target pixel evaluation values ft (1) to ft (n) and spatial correlation values fs ′ (1) to fs ′ (n) are calculated for (n), respectively (see FIG. 24).

  Specifically, as shown in FIG. 24, similarly to the target pixel evaluation unit 240C, the phase amplitude correction unit 244C performs the target pixel time density change NT (1) to NT (n) (or NT ′ (1). ˜NT ′ (n)) and the reference pixel time density change NR (1) to NR (n) (or NR ′ (1) to NR ′ (n)), the spatial correlation value calculation unit 246C ′ The second spatial correlation value calculation process is executed to calculate a plurality of spatial correlation values fs ′ (1) to fs ′ (n) corresponding to the plurality of target pixel evaluation values ft (1) to ft (n). .

<4-2-3. Period Evaluation Result Determination Unit 340C>
Subsequently, the cycle evaluation result determination unit 340C includes a statistical object pixel evaluation value calculation unit 341C (see FIG. 23).

  The statistical target pixel evaluation value calculation unit 341C has the same functional configuration as that of the statistical target pixel evaluation value calculation unit 341B (see FIG. 20), but is different from the statistical target pixel evaluation value calculation unit 341B in FIG. Thus, the statistical object pixel evaluation value St is calculated in consideration of the spatial correlation values fs (1) to fs (n).

  That is, in the statistical target pixel evaluation value calculation unit 341C (periodic evaluation result determination unit 340C), among the plurality of target pixel evaluation values ft (1) to ft (n), a plurality of spatial correlation values fs (1) to fs ( n) (d1) the sum of a predetermined number of effective target pixel evaluation values ft for a predetermined number of effective target pixel evaluation values ft or a predetermined number of effective target pixels TP determined to be effective based on n), and (d2) a predetermined number of Among the effective target pixel evaluation values ft, the sum of target pixel evaluation values ft that are equal to or greater than a predetermined value, (d3) the highest value among a predetermined number of effective target pixel evaluation values ft, and (d4) between a predetermined number of effective target pixel TPs The statistical predetermined pixel evaluation value St is calculated based on at least one of the statistical values (d1) to (d4) of the similarity of the target pixel time density change NT.

  Then, the cycle evaluation result determination unit 340C (cycle evaluation unit 140C) determines the statistical object pixel evaluation value St as the cycle evaluation result CR (see FIG. 22).

<4-2-4. Period Evaluation Result Determination Unit 340C ′>
Further, the period evaluation result determination unit 340C ′ includes a statistical object pixel evaluation value calculation unit 341C ′ (see FIG. 24).

  The statistical target pixel evaluation value calculation unit 341C ′ also has the same functional configuration as the statistical target pixel evaluation value calculation unit 341C (see FIG. 23), but is different from the statistical target pixel evaluation value calculation unit 341C in FIG. As shown, the statistical object pixel evaluation value St is calculated in consideration of the spatial correlation values fs ′ (1) to fs ′ (n). That is, in the statistical target pixel evaluation value calculation unit 341C ′ (periodic evaluation result determination unit 340C ′), among the plurality of target pixel evaluation values ft (1) to ft (n), a plurality of spatial correlation values fs ′ (1). At least one of the statistical values (d1) to (d4) for a predetermined number of effective target pixel evaluation values ft or a predetermined number of effective target pixels TP determined to be effective based on .about.fs' (n). The statistical object pixel evaluation value St is calculated based on the value.

  Then, the cycle evaluation result determination unit 340C ′ (cycle evaluation unit 140C ′) determines the statistical object pixel evaluation value St as the cycle evaluation result CR (see FIG. 22).

<4-3. Basic Operation of Image Processing Device 3C (3C ′)>
FIG. 25 is a diagram illustrating an operation flow of the image processing apparatus 3C (3C ′) according to the fourth embodiment. In FIG. 25, steps SC1, SC3, and SC5 are the same as steps SA1, SA3, and SA5 in FIG.

  In the fourth embodiment, unlike the second embodiment, only the following steps are changed by determining a plurality of target pixels TP.

  That is, as a process similar to the second embodiment, through step SC1, as shown in FIG. 25, in step SC2, in the pixel determining unit 120C, the target pixel determining unit 121C includes each of the plurality of frame images IG. In the target area TA, a plurality of target pixels TP (1) to TP (n) are determined, and the reference pixel determination unit 122C determines a plurality of reference pixels RP (1) to RP (n) corresponding thereto.

  In step SC4, the target pixel evaluation unit 240C (240C ′) of the cycle evaluation unit 140C (140C ′) performs the target pixel evaluation values ft (1) to ft (n) and the spatial correlation value for each blood flow cycle PC. fs (1) to fs (n) (fs ′ (1) to fs ′ (n)) are calculated, and the statistical object pixel evaluation value calculation unit 341C (341C ′) calculates the plurality of spatial correlation values fs (fs ′ ) For a predetermined number of effective target pixel evaluation values ft or a predetermined number of effective target pixels TP determined to be effective based on at least one of the statistical values (d1) to (d4). The target pixel evaluation value St is calculated (see FIGS. 23 and 24). Then, the cycle evaluation result determination unit 340C (340C ′) determines the statistical object pixel evaluation value St as the cycle evaluation result CR (see FIG. 22). The remaining steps are the same as in the second embodiment.

  As described above, in the image processing device 3C (3C ′) according to the fourth embodiment, a plurality of spatial correlation values fs (1) to fs (n) (fs ′ (1) to fs ′ (n)) are used. The effectiveness of the plurality of target pixel evaluation values ft is determined. As a result, the correlation between each of the plurality of target pixels TP and the surrounding pixels can be seen, and after determining whether the plurality of target pixel values are noise or significant signals, a predetermined number of determined to be valid Based on the valid target pixel evaluation value ft or a predetermined number of valid predetermined pixels TP, the statistical target pixel evaluation value St can be calculated with high accuracy.

<5. Fifth Embodiment>
FIG. 26 is a diagram showing a functional configuration of an image processing apparatus 3D configured as the fifth embodiment of the present invention. The difference from the first embodiment is that in the image processing device 3D, the specific cycle frame image SG selected by the cycle selection unit 150 is output to the storage unit 32, the display unit 34, and the communication unit 35 via the analysis unit 36. Is a point. That is, the analysis unit 36 performs moving image analysis processing on the specific cycle frame image SG, and then outputs the obtained specific cycle analysis image SA to the storage unit 32, the display unit 34, and the communication unit 35. The remaining configuration is the same as that of the image processing apparatus 3.

  The analysis unit 36 performs moving image analysis on the frame image including the specific cycle frame image SG, and obtains the moving image analysis result (specific cycle analysis image SA). Hereinafter, blood flow phase analysis will be described as an example of moving image analysis performed by the analysis unit 36.

<5-1. Blood flow phase analysis>
The analysis unit 36 obtains a specific cycle analysis image SA (moving image analysis result) by performing a blood flow phase analysis using the specific cycle frame image SG selected by the cycle selection unit 150. Here, the blood flow phase is phase information indicating the presence or absence of blood flow according to the position where the blood flow is flowing. Regarding blood flow phase analysis processing (blood flow information generation processing) used in the present invention, for example, “Japanese Patent Application No. 2011-115601 (filing date: May 24, 2011)” filed by the present applicant is adopted. can do.

  FIG. 27 is a diagram illustrating a blood flow phase analysis result, a diagram illustrating an analysis result of a spatio-temporal change associated with the presence or absence of blood flow in the entire lung, and a bar attached below the specific period analysis image SA. Indicates the presence or absence of blood flow. In general, in blood flow phase analysis, blood vessels are rapidly discharged from the right ventricle through the aorta due to contraction of the heart, so that the blood vessels in the lung expand. Therefore, this expansion is extracted by analyzing the dynamic image. This is output as diagnostic support information regarding the presence or absence of blood flow in the entire lung. That is, when a blood vessel dilates in the lung field, the amount of radiation transmitted in the region where the pulmonary blood vessel has expanded is relatively smaller than the amount of radiation transmitted through the lung field (alveolar region). The output signal value of the detector 13 decreases. The spread of pulmonary blood vessels in response to the pulsation of the heart propagates from the artery near the heart to the periphery. Therefore, the pixel (pixel) unit of the radiation detection unit 13 between a series of specific periodic frame images SG constituting the dynamic image, or a small area unit (pixel block unit) composed of a plurality of pixels is associated with each other, so For each region unit, the frame image SG having the lowest signal value is obtained, and the corresponding region of the frame image SG is colored as a signal indicating the timing when the pulmonary blood vessels are expanded by blood flow. Then, as shown in FIG. 27, a series of specific period analysis images SA (specific period frame images SG) after coloring are sequentially displayed on the display unit 34 so that a doctor or the like can visually recognize the state of blood flow. Become. Note that the white portions shown in FIG. 27 are actually colored in red or the like.

  In each pixel (small region), a signal (referred to as a blood flow signal) indicating the timing at which a pulmonary blood vessel is expanded by blood flow is a waveform (referred to as an output signal waveform) indicating a temporal change in the signal value of that pixel (small region). It can be obtained by obtaining the minimum value. This blood flow signal appears at the same interval as the heartbeat cycle, but if there is an abnormal part such as an arrhythmia, a minimum value appears at a different interval from the heart beat cycle regardless of blood vessel dilation There is. Therefore, the blood flow signal is accurately extracted by obtaining the correlation coefficient between the pulsation signal waveform indicating the pulsation of the heart and the output signal waveform of each small region.

  In the following, an outline of an output signal waveform of the pulmonary blood vessel region and a blood flow signal extraction method will be described as detection of blood flow information in the pulmonary blood vessel region. FIG. 28 is a diagram illustrating a waveform showing a temporal change in a blood flow signal value in a pulmonary blood vessel region. FIG. 28A shows the position of the pulmonary blood vessel region IR corresponding to the diagnosis target region in a series of specific periodic frame images SG acquired sequentially in time. In FIG. 28B, the horizontal axis shows the dynamic image. The signal value (representative value) of the pulmonary vascular region IR of each frame image SG is plotted on the coordinate space where the elapsed time (frame number) from the start of imaging is taken and the vertical axis is the signal value (representative value) in the pulmonary vascular region IR. The graph is shown.

  Here, in the graph of FIG. 28B, since the phases of respiration and blood flow are mixed, the influences of respiration and blood flow are excluded by the following filtering process. That is, in the filtering process, a low-frequency signal change due to breathing or the like is removed, and a temporal change in the signal value due to blood flow is extracted. For example, with respect to the time change of the signal value for each small region, high-pass filtering is performed at a low-frequency cut-off frequency of 0.7 Hz in the quiet breathing image group and at a low-frequency cut-off frequency of 0.5 Hz in the deep-breathing image group. Alternatively, filtering may be performed by a band-pass filter that cuts off a high frequency at a high-frequency cutoff frequency of 2.5 Hz in order to remove a noise component at a higher frequency.

  Here, the cutoff frequency is preferably optimized for each captured dynamic image, rather than a fixed value. For example, the systolic time and the diastolic (relaxation) time of the heart are calculated from signal changes in the heart region of a series of specific periodic frame images SG (see FIG. 28A). A value obtained by multiplying the reciprocal of the diastole time by a predetermined coefficient is set as a cut-off frequency for cutting off low frequencies with a high-pass filter or a band-pass filter. In the case of a band-pass filter, the value of the systolic time is set. A value obtained by multiplying the reciprocal by a predetermined coefficient is set as a high-frequency cutoff frequency that cuts off the high frequency. In addition, the low frequency cut-off frequency considers the frequency component due to respiration, analyzes the position value of the diaphragm, the area value of the lung field region or the distance between feature points from a series of specific periodic frame images SG, and in the case of rest ventilation Detects the frame image SG at the resting expiratory position and the resting inspiratory position, obtains the time of the inspiratory period from the number of frames between the frame of the resting expiratory position and the frame of the next resting inspiratory position, the reciprocal thereof and the expansion A value obtained by multiplying the average value of the period time by a predetermined coefficient may be set as the low-frequency cutoff frequency. At this time, in the case of rest ventilation, the cut-off frequency that is automatically set is limited to a low cut-off frequency of 0.2 to 1.0 Hz and a high cut-off frequency of 2.0 Hz or more. Is preferred. Alternatively, vital signs such as a one-minute respiratory rate and pulse rate measured at rest may be input as patient information, and the cutoff frequency may be calculated from these values. For example, a low frequency cut-off frequency may be a value obtained by converting a respiration rate per minute input as patient information into a respiration rate per second and multiplying the respiration rate by a predetermined coefficient. Then, the input 1-minute pulse rate may be converted into a 1-second pulse rate, and a value obtained by multiplying the 1-second respiration rate by a predetermined coefficient may be used as the high-frequency cutoff frequency. Further, a value obtained by multiplying the average value of the respiration rate for one second and the heart rate for one second by a predetermined coefficient may be set as the low-frequency cutoff frequency.

<5-2. Example of blood flow diagnosis using analysis unit 36>
FIG. 29 is a diagram illustrating an example of blood flow diagnosis. When the affected area is known, the target pixel TP is determined by setting the affected area as the target area TA and a specific blood flow cycle SPC is selected. However, when the affected area is unknown, the target area TA cannot be clearly specified.

  Therefore, as shown in FIG. 29, the target area TA (that is, the target pixel TP) is sequentially changed and determined while moving along the blood vessel BV. Specifically, the region TA1 (pixel TP1), the region TA2 (pixel TP2), the region TA2 (pixel TP2), and the region TA3 (pixel TP3) are sequentially aligned along the bloodstream from the upstream of the blood flow (side closer to the heart). The specific periodic frame images SG1, SG2, SG3 are obtained sequentially for each of the target areas TA1, TA2, TA3, and the specific periodic frame images SG1, SG2, SG3 are displayed on the display unit 34. .

  Or the analysis part 36 displays specific period analysis image SA1, SA2, SA3 on the display part 34 by performing a blood-flow phase analysis with respect to specific period frame image SG1, SG2, SG3, respectively.

  Here, the display method of the specific cycle frame images SG1, SG2, SG3 or the specific cycle analysis images SA1, SA2, SA3 is displayed adjacent to each of the target areas TA1, TA2, TA3, as well as the specific cycle analysis images SA1, SA2, and SA3. SA3 may be superimposed (see FIG. 29). Thereby, it is possible to obtain an image showing the affected area from the specific period analysis images SA1, SA2, SA3 or the specific period analysis images SA1, SA2, SA3, and it is possible to find the affected area such as vascular stenosis. .

<5-3. Basic Operation of Image Processing Device 3D>
Subsequently, FIG. 30 is a diagram illustrating an operation flow of the image processing device 3D according to the fifth embodiment. In FIG. 30, steps SD1 to SD4 are the same as steps S1 to S4 in FIG.

  In the fifth embodiment, unlike the first embodiment, since the data is output to the storage unit 32, the display unit 34, and the communication unit 35 via the analysis unit 36, only the following steps are changed.

  That is, as a process similar to the first embodiment, through steps SD1 to SD4, as shown in FIG. 31, in step SD5, the cycle selection unit 150 is based on the cycle evaluation result CR obtained in step SD4. After obtaining the specific periodic frame image SG, the analysis unit 36 performs moving image analysis on the specific periodic frame image SG (see FIGS. 27 and 28).

  In step SD6, the analysis unit 36 obtains the moving image analysis result (specific period analysis image SA) and outputs it to the storage unit 32, the display unit 34, and the communication unit 35 (see FIG. 26), and this operation flow is finished. .

  As described above, in the image processing device 3D according to the fifth embodiment, the analysis processing time can be shortened by performing the moving image analysis only on the specific periodic frame image SG.

  Further, by displaying only the specific cycle analysis image SA (moving image analysis result) for the specific cycle frame image SG, the diagnosis time can be shortened.

  On the other hand, moving image analysis is also performed on the frame image IG other than the specific blood flow period SPC, only the specific period analysis image SA of the specific blood flow period SPC is displayed at a normal speed, and the moving images of the other frame images IG are displayed. The analysis result can be displayed at high speed or thinned out.

  Further, by storing only the specific cycle analysis image SA for the specific cycle frame image SG, the storage area can be reduced, and the storage capacity can be saved. On the other hand, moving image analysis is also performed on the frame image IG other than the specific blood flow cycle SPC, and only the specific cycle analysis image SA of the specific blood flow cycle SPC is reversibly (non-) compressed and stored. It is possible to perform lossy compression storage for moving image analysis results other than.

  Further, by communicating only the specific cycle analysis image SA with respect to the specific cycle frame image SG, it is possible to suppress the communication load and shorten the communication time. On the other hand, the moving image analysis is performed also in the frame image IG other than the specific blood flow cycle SPC, and only the specific cycle analysis image SA of the specific blood flow cycle SPC is reversibly (non-) compressed to transmit the specific blood flow cycle SPC. It is possible to perform lossy compression transmission for moving image analysis results other than.

  Further, the target pixel determining unit 121 sequentially changes and determines the target pixel TP while moving along the blood vessel, and adjoins the specific cycle frame image SG or the specific cycle analysis image SA obtained for each changed target pixel TP. Display layout or overlay. Thereby, even when the affected part cannot be specified, the affected part can be sequentially searched by the superimposed display, and an efficient medical diagnosis can be performed.

<6. Sixth Embodiment>
FIG. 31 is a diagram illustrating a functional configuration of the control unit 31E used in the image processing device 3E configured as the sixth embodiment of the present invention. The control unit 31E is used as an alternative to the control unit 31 (see FIG. 4) in the image processing apparatus 3 of the first embodiment. The difference from the first embodiment is that a position correspondence processing unit 115 is added. The remaining configuration is the same as that of the image processing apparatus 3.

<6-1. Position correspondence processing unit 115>
FIG. 32 is a schematic diagram for explaining the spatial positional relationship of each pixel between the frame images IG when the body of the subject M moves spatially during moving image shooting. Here, FIG. 32A shows a temporal density change of the pixel PX in the frame image IG, and FIG. 32B shows a spatial positional relationship in the frame image IG of the pixel PX separated by time.

  As shown in FIG. 32A, among the temporal density changes of the pixel PX, the pixel PX of the frame image IG at the time t = ta is defined as the pixel PXa, and the pixel PX of the frame image IG at the time t = tb is defined as the pixel PXb. And the pixel PX of the frame image IG at time t = tc is defined as a pixel PXc. If the body of the subject M does not move spatially during moving image shooting, the spatial positions of the frame images IG of the pixels PXa, PXb, and PXc are naturally the same. However, when the body of the subject M moves spatially during imaging, the spatial positions of the frame images IG of the pixels PXa, PXb, and PXc do not match as shown in FIG.

  Thus, it is assumed that the body of the subject M moves spatially during moving image shooting, so that each pixel in the frame image IG moves spatially when viewed in the time direction. As a result, even if the target pixel TP is determined, different positions of the body are designated between the frame images IG, and accurate analysis cannot be performed.

  Therefore, in the case where the body of the subject M moves spatially during the shooting of the moving image, the position correspondence processing unit 115 causes the spatial position of each pixel between the frame images IG immediately after being acquired by the moving image acquisition unit 110. Is performed in the time direction, and the processed frame image IG is output to the pixel determining unit 120.

  Specifically, the position correspondence processing unit 115 tracks and associates the movement of the subject M between the frame images IG. Here, as a method of tracking and associating between the respective frame images IG, for example, an existing method of corresponding point search processing or the like (for example, refer to Japanese Patent Application Laid-Open No. 2012-079251 by the applicant) is adopted. Can do.

  The corresponding point search process is obtained by searching for a point (corresponding point) on the corresponding point reference image corresponding to an arbitrary target point on the corresponding point reference image. The corresponding point reference image is an image corresponding to the corresponding point standard image. Specifically, among the captured moving images, the temporally previous frame image IG is the corresponding point reference image, and the temporally subsequent frame image IG is the corresponding point reference image. For example, in corresponding point search by template matching, a template is set for a point of interest on the corresponding point reference image, a window on the corresponding point reference image corresponding to the template is searched, and the corresponding window is searched from the searched window. A point is required.

<6-3. Basic Operation of Image Processing Device 3E>
Subsequently, FIG. 33 is a diagram illustrating an operation flow of the image processing apparatus 3E according to the sixth embodiment. Here, it is assumed that the body of the subject M moves spatially during moving image shooting. In FIG. 33, steps SE1, SE3 to SE6 are the same as steps S1 to S5 of FIG.

  In the sixth embodiment, unlike the first embodiment, the following process is added by adding the position correspondence processing unit 115.

  That is, as a process similar to that of the first embodiment, after step SE1, as shown in FIG. 33, in step SE2, the position correspondence processing unit 115 causes each pixel between the frame images IG acquired in step SE1. Is performed, and the frame image IG after the processing is output to the pixel determining unit 120 (see FIGS. 31 and 32). The remaining steps are the same as in the first embodiment.

  As described above, in the image processing device 3E according to the sixth embodiment, when the body of the subject M moves spatially during shooting of a moving image, the frame image IG immediately after being acquired by the moving image acquisition unit 110 The image processing apparatus further includes a position correspondence processing unit 115 that performs processing for associating the spatial position of each pixel in the time direction and outputs the frame image IG after the processing to the pixel determination unit 120. That is, it is possible to correct the positional relationship in the time direction of the target pixel TP between the frame images IG by correcting the spatial movement of the body by corresponding point search processing or the like. Thereby, even if the body moves spatially during imaging, accurate medical diagnosis can be performed with respect to the state change of the target area TA within the specific blood flow cycle SPC.

<7. 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 this embodiment, the image processing apparatuses 3, 3 ′, 3A (3A ′), 3B, 3C (3C ′), 3D, and 3E are described separately so that they are individually implemented. The individual functions may be combined with each other as long as they do not contradict each other.

  * In this embodiment, step S2 (SA2, SB2, SC2, SD2, SE2) is performed and then step S3 (SA3, SB3, SC3, SD3, SE3) is performed, but this order is reversed. It is also possible.

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

DESCRIPTION OF SYMBOLS 1 Shooting device 2 Shooting control device 3, 3 ′, 3A (3A ′), 3B, 3C (3C ′), 3D, 3E Image processing devices 31, 31 ′, 31A (31A ′), 31B, 31C (31C ′) , 31D, 31E Control unit 32 Storage unit 34 Display unit 35 Communication unit 36 Analysis unit 100, 100 ′, 100A (100A ′), 100B, 100C (100C ′), 100D, 100E Radiation dynamic imaging system 110 Moving image acquisition unit 115 Position correspondence processing unit 120, 120A, 120B, 120C Pixel determination unit 121, 121A, 121B, 121C Target pixel determination unit 122, 121C Reference pixel determination unit 130 Period classification unit 140, 140A (140A ′), 140B, 140C (140C) '), 140D, 140E Period evaluation section 240, 240A (240A'), 240B, 240C 240C ′) Target pixel evaluation unit 244 Phase amplitude correction unit 340, 340A (340A ′), 340B, 340C (340C ′) Period evaluation result determination unit 150 Period selection unit TA Target region TP Target pixel TN Target pixel Time density change ft Target Pixel evaluation value RP Reference pixel RN Reference pixel time density change fr Reference pixel evaluation value fs, fs' Spatial correlation value St Statistical target pixel evaluation value M Subject (subject)
IG frame image SG specific cycle frame image SA specific cycle analysis image PC blood flow cycle SPC specific blood flow cycle CR cycle evaluation result

Claims (18)

A moving image acquisition means for acquiring a moving image composed of a plurality of frame images capturing a state in which a physical state of a predetermined region in a human body or an animal body periodically changes in time every frame shooting period;
A pixel determining unit including a predetermined pixel determining unit that determines a predetermined pixel in each predetermined region of the plurality of frame images;
A period classification unit that detects a predetermined area period that is a periodic time change of the predetermined area, and classifies the plurality of frame images into the predetermined area period units;
A period evaluation unit that includes a predetermined pixel evaluation unit that evaluates for each predetermined area period based on a predetermined pixel value that is a pixel value of the predetermined pixel, and obtains a period evaluation result of the predetermined pixel for each predetermined area period;
A period selecting means for selecting a specific predetermined area period based on the period evaluation result and setting the frame image classified into the specific predetermined area period as a specific period frame image;
Further comprising:
Image processing device. The image processing apparatus according to claim 1,
The predetermined pixel evaluation unit includes:
A predetermined pixel time density change calculating unit that calculates a predetermined pixel time density change indicating a density change in the time direction of the predetermined pixel value within the predetermined region period;
(A1) At least one value among the maximum value and the minimum value in the predetermined pixel time density change,
(A2) at least one value among the maximum differential value and the minimum differential value in the predetermined pixel time density change,
(A3) an extreme value in the predetermined pixel time density change,
(A4) The number of frame images in which the predetermined pixel value is equal to or greater than a first predetermined value among the frame images that generate the predetermined pixel time density change,
(A5) a pixel integration value obtained by integrating pixel values at a second predetermined value or more in the predetermined pixel time density change in the time direction;
(A6) a correlation value between the predetermined pixel time density change and a reference change model prepared in advance;
A predetermined pixel evaluation value calculation unit that calculates a predetermined pixel evaluation value based on at least one of the determination elements (a1) to (a6),
Including
The period evaluation means includes
A cycle evaluation result determination unit that determines the predetermined pixel evaluation value as the cycle evaluation result;
Image processing device. The image processing apparatus according to claim 2,
The reference change model is
It is created using a spatial density change indicating a density change in a spatial direction of pixel values of a plurality of pixels along a blood flow direction in a blood vessel region on the frame image,
Image processing device. The image processing apparatus according to claim 2,
The pixel determining means includes
A reference pixel determining unit that determines a reference pixel different from the predetermined pixel on the frame image;
Further including
The predetermined pixel evaluation unit includes:
A reference pixel time density change calculating unit for calculating a reference pixel time density change indicating a density change with respect to a time direction of a reference pixel value that is a pixel value of the reference pixel within the predetermined region period;
(B1) At least one value of the maximum value and the minimum value in the reference pixel time density change,
(B2) At least one value of the maximum differential value and the minimum differential value in the reference pixel time density change,
(B3) at least one of a maximum value and a minimum value in the reference pixel time density change,
(B4) The number of frame images in which the reference pixel value is equal to or greater than a third predetermined value among the frame images constituting the reference pixel time density change,
(B5) a pixel integration value obtained by integrating pixel values at a fourth predetermined value or more in the reference pixel time density change in the time direction;
(B6) a correlation value between the reference pixel time density change and a reference change model prepared in advance;
Reference pixels to be calculated as reference pixel evaluation values based on the determination elements corresponding to the determination elements (a1) to (a6) used for calculating the predetermined pixel evaluation value among the determination elements (b1) to (b6) An evaluation value calculation unit;
A spatial correlation value calculation unit that executes a first spatial correlation value calculation process that calculates a difference between the predetermined pixel evaluation value and the reference pixel evaluation value as a spatial correlation value;
Further including
The period evaluation result determination unit
The effectiveness of the period evaluation result is determined based on the spatial correlation value,
Image processing device. The image processing apparatus according to claim 2,
The pixel determining means includes
A reference pixel determining unit that determines a reference pixel different from the predetermined pixel on the frame image;
Further including
The predetermined pixel evaluation unit includes:
A reference pixel time density change calculating unit for calculating a reference pixel time density change indicating a density change in the time direction of the reference pixel value in the predetermined region period;
A total sum of differences between the predetermined pixel value and the reference pixel value obtained by comparing the periodic change of the predetermined pixel time density change and the periodic change of the reference pixel time density change is calculated as a spatial correlation value. A spatial correlation value calculation unit that executes the spatial correlation value calculation process of 2;
Further including
The period evaluation result determination unit
The effectiveness of the period evaluation result is determined based on the spatial correlation value,
Image processing device. The image processing apparatus according to claim 4 or 5, wherein
The predetermined pixel evaluation unit, as preprocessing for obtaining the spatial correlation value,
(C1) correction for matching a shift between the phase of the predetermined pixel time density change and the phase of the reference pixel time density change;
(C2) Correction for adjusting the phase and amplitude of the predetermined pixel time density change and / or the reference pixel time density change according to the distance from the heart region,
A phase amplitude correction unit that performs at least one of the corrections (c1) and (c2) after calculating the reference pixel time density change,
Further including
Image processing device. The image processing apparatus according to claim 4,
The reference pixel determining unit includes:
A pixel whose spatial distance to the predetermined pixel is a predetermined distance value or less is determined as the reference pixel,
Image processing device. The image processing apparatus according to claim 4,
The reference pixel determining unit determines a plurality of reference candidate pixels that are candidates for the reference pixel,
The predetermined pixel evaluation unit includes:
A reference candidate pixel time density change indicating a density change with respect to a time direction of a reference candidate pixel value which is a pixel value of the reference candidate pixel within the predetermined region period is calculated, and the maximum value among the reference candidate pixel time density changes is A change that satisfies a predetermined threshold or more is determined as a reference pixel time density change;
Image processing device. The image processing apparatus according to claim 2,
The predetermined pixel includes a plurality of pixels, the predetermined pixel evaluation value includes a plurality of predetermined pixel evaluation values, and the predetermined pixel evaluation value calculation unit calculates the plurality of predetermined pixel evaluation values for the plurality of predetermined pixels. And
The period evaluation means includes
(D1) the sum of the plurality of predetermined pixel evaluation values;
(D2) among the plurality of predetermined pixel evaluation values, a total sum of the predetermined pixel evaluation values equal to or greater than a predetermined value;
(D3) the highest value among the plurality of predetermined pixel evaluation values;
(D4) similarity of the predetermined pixel time density change between the plurality of predetermined pixels;
A statistical predetermined pixel evaluation value calculation unit that calculates a statistical predetermined pixel evaluation value based on at least one of the statistical values (d1) to (d4),
Further including
The period evaluation means includes
A cycle evaluation result determination unit that determines the statistical predetermined pixel evaluation value as the cycle evaluation result;
Image processing device. The image processing apparatus according to claim 4 or 5, wherein
The predetermined pixel includes a plurality of pixels, the predetermined pixel evaluation value includes a plurality of predetermined pixel evaluation values, and the predetermined pixel evaluation value calculation unit calculates the plurality of predetermined pixel evaluation values for the plurality of predetermined pixels. And
The reference pixel is different from the plurality of predetermined pixels and includes a plurality of reference pixels corresponding to the plurality of predetermined pixels, and the spatial correlation value calculation unit performs the first or second space with respect to the plurality of reference pixels. Performing a correlation value calculation process to calculate the plurality of spatial correlation values corresponding to the plurality of predetermined pixel evaluation values;
The period evaluation unit is configured to output a predetermined number of effective predetermined pixel evaluation values or a predetermined number of effective predetermined pixels determined to be effective based on the plurality of spatial correlation values among the plurality of predetermined pixel evaluation values.
(D1) a sum of the predetermined number of effective predetermined pixel evaluation values;
(D2) among the predetermined number of effective predetermined pixel evaluation values, a sum of the predetermined pixel evaluation values equal to or greater than a predetermined value;
(D3) the highest value of the predetermined number of effective predetermined pixel evaluation values;
(D4) similarity of the predetermined pixel time density change between the predetermined number of effective predetermined pixels;
A statistical predetermined pixel evaluation value calculation unit that calculates a statistical predetermined pixel evaluation value based on at least one of the statistical values (d1) to (d4),
Further including
The period evaluation means includes
A cycle evaluation result determination unit that determines the statistical predetermined pixel evaluation value as the cycle evaluation result;
Image processing device. An image processing apparatus according to any one of claims 1 to 10, wherein
Analyzing means for performing a moving image analysis on a frame image including the specific periodic frame image, and obtaining the moving image analysis result;
Further comprising
Image processing device. The image processing apparatus according to claim 11,
Display means for displaying the specific periodic frame image or the moving image analysis result;
Further comprising
Image processing device. The image processing apparatus according to claim 11,
Storage means for storing the specific periodic frame image or the moving image analysis result;
Further comprising
Image processing device. The image processing apparatus according to claim 11,
Communication means for communicating the specific periodic frame image or the moving image analysis result;
Further comprising
Image processing device. The image processing device according to any one of claims 1 to 14,
The predetermined region includes a blood vessel region,
Image processing device. The image processing apparatus according to claim 11,
The predetermined pixel determining unit sequentially changes and determines predetermined pixels while moving along a blood vessel, and the specific periodic frame image or the moving image analysis result obtained for each of the changed predetermined pixels is displayed adjacently or Display means for superimposed display;
Further comprising
Image processing device. The image processing device according to any one of claims 1 to 16, wherein
In the case where the body moves spatially during the shooting of the moving image,
Position correspondence processing means for performing processing for associating the spatial position of each pixel between the frame images immediately after acquired by the moving image acquisition means in the time direction, and outputting the processed frame image to the pixel determination means ,
Further comprising
Image processing device.   A program for causing a computer to function as the image processing apparatus according to any one of claims 1 to 17, when executed by a computer included in the image processing apparatus.