JP2019025296A - Image processing device, image processing method, and program - Google Patents

Abstract

To provide an image processing system, an image processing method, and a program capable of more precise positioning with respect to a coordinate system of a reference associated with an anatomical position.SOLUTION: A device acquires section definition information which defines a plurality of sections of a human body segmented along the body axis, and acquires three-dimensional images including a plurality of slice images showing a cross section of the subject. On the basis of the section definition information, a section for the cross section corresponding to at least one slice image included in the three-dimensional images is specified. A coordinate value of the slice image is calculated on the basis of the specified section and a reference coordinate system in which a coordinate value is defined for each of the sections.SELECTED DRAWING: Figure 7

Description

  The present invention relates to an image processing apparatus, an image processing method, and a program.

  In the medical field, diagnosis using three-dimensional images acquired by various imaging devices (modalities) such as a computed tomography apparatus (hereinafter referred to as a CT (Computed Tomography) apparatus) is performed. In particular, when capturing changes in the state of a subject over time, images taken at different times are compared. At this time, when a certain slice of one image (an image obtained by cutting out a three-dimensional image with a certain cross section) is displayed, a slice (hereinafter referred to as a corresponding slice) including substantially the same anatomical structure as that slice in the other image is displayed. Image) can be easily compared. For this purpose, for each slice of the images to be compared, it is necessary to associate corresponding slices between images taken at different times in advance. Here, “alignment” in this article refers to associating slices including substantially the same anatomical structure (that is, corresponding slices) between a plurality of three-dimensional images with respect to each slice constituting the three-dimensional image. To do.

  However, since the number of slices of a plurality of images to be diagnosed often reaches several hundreds or more, performing this alignment manually has the problem of imposing a heavy burden on the operator. It was.

  In view of the above problems, Patent Document 1 discloses a comparison object by using a reference coordinate system associated with an anatomical position when performing alignment between images captured at a plurality of time points. Even when there are three or more images, a method is disclosed in which they can be easily aligned. However, if the balance of the size of the anatomical structure existing between the anatomical positions is different between the images to be aligned, slices that show the same coordinate value in the reference coordinate system are between the images. There is a possibility that the corresponding position is not reached. As one method for solving this problem, Patent Document 1 further discloses the following method. The method is to prepare a plurality of coordinate systems according to the size of the anatomical structure (skeleton, age, height, gender, etc.) and select an appropriate coordinate system for each image to be processed. By doing so, this is a method for realizing a more accurate alignment.

Japanese Patent No. 4818846

  As described above, in the technique described in Patent Document 1, when only one reference coordinate system is used, if the balance of the size of the anatomical structure of the subject is different between different images, Slice alignment may not be possible. Further, in order to improve the problem and perform alignment with higher accuracy, it is necessary to select which coordinate system to use from the plurality of coordinate systems.

  The present invention has been made in view of the above problems, and provides an image processing apparatus, an image processing method, and a program that can perform alignment with higher accuracy with respect to a reference coordinate system associated with an anatomical position. For the purpose.

  It is to be noted that the present invention is not limited to the above-described object, and is an operational effect derived from each configuration shown in an embodiment for carrying out the invention described later, and also has an operational effect that cannot be obtained by conventional techniques. It can be positioned as one of other purposes.

  In order to solve the above problems, an image processing apparatus according to the present invention includes information acquisition means for acquiring section definition information for defining a plurality of sections obtained by dividing a human body along the body axis direction, and a plurality of sections showing a cross section of a subject. And an image acquisition means for acquiring a three-dimensional image including the slice image, and a section specification for specifying the section to which the cross section corresponding to at least one slice image included in the image belongs based on the section definition information And a coordinate value calculating means for calculating the coordinate value of the slice image based on a reference coordinate system in which coordinate values are defined for each section and the specified section.

  According to the present invention, it is possible to perform alignment with higher accuracy with respect to a reference coordinate system associated with an anatomical position.

It is a figure which shows the structure of the function of the image processing apparatus which concerns on 1st Embodiment. It is a flowchart which shows the example of the process sequence of an image processing apparatus. It is a figure which shows the example of the section definition information acquired by the section definition information acquisition part. It is a figure which shows the example which displayed the image acquired by the image acquisition part on the display part. It is a figure which shows the example with which a certain image I1 is comprised by the slice showing from the vertex of a cervical vertebra to the lowest point of a sciatica. It is a figure which shows the example which defined the reference coordinate system Z 'with respect to each area. FIG. 6 is a diagram illustrating an example of a case where a coordinate system z and a reference coordinate system Z ′ with the uppermost position of the image I1 as the origin in the example illustrated in FIG. 5 are considered. It is a flowchart which shows the 2nd example of the process sequence of an image processing apparatus. It is a figure which shows the example of the relationship between the range of a certain image, and the estimation result of the position of an anatomical feature cross section. It is a figure which shows the example of the result of estimating the position of the anatomical feature cross section out of the range of an input image, and the coordinate value in the coordinate system which made the highest position (slice) of the input image the origin.

  Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings. However, the components described in this embodiment are merely examples, and the technical scope of the present invention is determined by the scope of the claims, and is not limited by the following individual embodiments. Absent.

<First Embodiment>
The image processing apparatus according to the first embodiment is an apparatus that displays a plurality of three-dimensional images. This apparatus uses a coordinate system (reference coordinate system) defined based on positions having anatomical features for each slice of the input image in order to display corresponding slices between different three-dimensional images. It has a function to calculate coordinate values. In the process of obtaining the coordinate value of each slice in the reference coordinate system, the apparatus divides the human anatomical structure into a plurality of sections in the body axis direction, and uses the information of the sections in each section to calculate the coordinates. It is characterized by calculating a value.

  According to this technique, by normalizing the position of the anatomical structure of the human body for each part in the body axis direction, it is possible to absorb the difference in the balance of the sizes of the plurality of anatomical structures (individual differences). In the alignment process, a more accurate alignment result can be provided to the user.

  Hereinafter, the configuration and processing of the image processing apparatus of this embodiment will be described with reference to FIG. FIG. 1 is a block diagram illustrating a configuration example of an image processing system (also referred to as a medical image processing system) including an image processing apparatus according to the present embodiment. The image processing system includes an image processing apparatus 10, a network 21, and a database 22 as functional configurations. The image processing apparatus 10 is communicably connected to the database 22 via the network 21. The network 21 includes, for example, a LAN (Local Area Network) and a WAN (Wide Area Network).

  The database 22 holds and manages the image of the subject and information associated with the image. The image processing apparatus 10 can acquire images held in the database 22 via the network 21. The image processing apparatus 10 includes a communication IF (Interface) 31 (communication unit), a ROM (Read Only Memory) 32, a RAM (Random Access Memory) 33, a storage unit 34, an operation unit 35, a display unit 36, and a control unit 37. Prepare.

  The communication IF 31 (communication unit) is configured by a LAN card or the like, and realizes communication between an external device (for example, the database 22 and the like) and the image processing apparatus 10. The ROM 32 is configured by a nonvolatile memory or the like, and stores various programs. The RAM 33 is configured by a volatile memory or the like, and temporarily stores various information as data. The storage unit 34 includes an HDD (Hard Disk Drive) or the like, and stores various types of information as data. The operation unit 35 includes a keyboard, a mouse, a touch panel, and the like, and inputs instructions from a user (for example, a doctor) to various devices.

  The display unit 36 is configured by a display or the like, and displays various information to the user. The control unit 37 is configured by a CPU (Central Processing Unit) and the like, and comprehensively controls processing in the image processing apparatus 10. The control unit 37 includes a section definition information acquisition unit 50, an image acquisition unit 51, a section identification unit 52, a coordinate calculation unit 53, a positioning unit 54, and a display processing unit 55 as functional configurations.

  The section definition information acquisition unit 50 acquires section definition information from the database 22. The section definition information is information for dividing the body axis direction of the human body into a plurality of sections based on an anatomical feature cross section including a predetermined anatomical feature of the human body. That is, the section definition information acquisition unit 50 corresponds to an example of an information acquisition unit that acquires section definition information that defines a plurality of sections obtained by dividing a human body along the body axis direction. Here, the “anatomical feature cross section” in this paper is, for example, an anatomical feature that appears at the boundary position of a group of anatomical structures (for example, parts) when the anatomical structure is viewed in the body axis direction of the human body. Refers to the drawn cross section. That is, the entire anatomical structure is divided into a plurality of types of parts (corresponding to the sections) in the body axis direction by the anatomical feature cross section. The location and number of the anatomical feature cross sections, that is, the number of divisions of a plurality of sections may be read in advance in the database 22. Alternatively, information input by the user via the operation unit 35 can be used.

  The image acquisition unit 51 acquires two or more images (images I 1, I 2,..., IN (N is the total number of images)) to be aligned from the database 22. That is, the image acquisition unit 51 corresponds to an example of an image acquisition unit that acquires a three-dimensional image including a plurality of slice images showing a cross section of a subject. These images are three-dimensional images of the subject acquired by various modalities. In the present embodiment, an example in which this image is a CT image will be described, but other types of images may be used. The present invention can be applied to any three-dimensional image regardless of the type of image. The plurality of images may be any images as long as they are images to be compared. That is, it may be an image of the same subject, or may be an image of another person, for example, an image of a healthy person and a patient.

  The section identifying unit 52 identifies, for each image from the images I1 to IN, which section the slice belongs to among the plurality of sections for at least one slice constituting the image. That is, the section identification unit 52 corresponds to an example of a section specifying unit that specifies a section to which a cross section corresponding to at least one slice image included in the image belongs (hereinafter referred to as a dependent section) based on the section definition information. . The identification of the dependent section can be realized by an estimation process using information acquired by the section definition information acquisition unit 50, information on pixel values constituting the slice, and information on feature amounts.

  The coordinate calculation unit 53 calculates the coordinate value of the slice image in the reference coordinate system corresponding to the dependent section based on the information of the dependent section identified by the section identifying unit 52. That is, the coordinate calculation unit 53 corresponds to an example of a coordinate value calculation unit that calculates the coordinate value of the slice image based on the reference coordinate system in which the coordinate value is defined for each section and the specified section.

  The alignment unit 54 calculates the position of the corresponding slice in the images I1 to IN based on the coordinate value calculated by the coordinate calculation unit 53. That is, the images I1 to IN are aligned. That is, the alignment unit 54 corresponds to an example of an association unit that associates each slice image having substantially the same coordinate value in the reference coordinate system included in the plurality of acquired images.

  Based on the result calculated by the alignment unit 54, the display processing unit 55 displays at least two of the input images I1 to IN so that the corresponding slices can be easily compared. In the form, it is displayed in the image display area of the display unit 36.

  Each component of the image processing apparatus 10 functions according to a computer program. For example, the function of each component is realized by the control unit 37 (CPU) reading and executing a computer program stored in the ROM 32 or the storage unit 34 using the RAM 33 as a work area. Note that some or all of the functions of the components of the image processing apparatus 10 may be realized by using a dedicated circuit. Further, some functions of the components of the control unit 37 may be realized by using a cloud computer.

  For example, an arithmetic device at a location different from the image processing device 10 is communicably connected to the image processing device 10 via the network 21, and the image processing device 10 and the arithmetic device transmit and receive data, whereby the image processing device 10 or functions of the components of the control unit 37 may be realized.

  Next, an example of processing of the image processing apparatus 10 in FIG. 1 will be described with reference to FIGS.

  FIG. 2 is a flowchart illustrating an example of a processing procedure of the image processing apparatus 10. In the present embodiment, an example based on the anatomical features of bones will be described. However, the present embodiment can also be applied when other attention sites are set.

(Step S101: Acquisition of section definition information for dividing the body axis direction of the human body into a plurality of sections)
In step S <b> 101, when the user instructs the start of image processing via the operation unit 35, the section definition information acquisition unit 50 reads section definition information for dividing the human anatomy into a plurality of sections in the body axis direction from the database 22. Obtained and stored in the RAM 33.

  Here, the section definition information is a combination of information on the positions of a plurality of anatomical feature sections and information on sections sectioned by these sections, and refers to information as shown in FIG. 3, for example. . In FIG. 3, based on information on the structure of the bones of the human body (the top section of the section including the cervical vertebra, the thoracic vertebra, and the lumbar vertebra), The example divided | segmented into the area is shown. More specifically, section 1 represents the interval from the top of the head to the position of the highest slice of the cervical spine, and section 2 represents the interval from the position of the highest slice of the cervical spine to the position of the highest slice of the thoracic vertebra. Yes. Hereinafter, similarly to the section 7 indicating the distance from the bottom surface of the femur to the sole of the foot, the body axis direction of the human body is divided into certain sections based on the cross section indicating the anatomical features. In addition, the anatomical feature cross section for performing the section division is not limited to this example, and a cross section based on the position of another organ may be used.

(Step S102: Image acquisition / display)
In step S101, when the user instructs acquisition of an image (I1 to IN) via the operation unit 35, the image acquisition unit 51 acquires the image IN from the image I1 specified by the user from the database 22 and stores it in the RAM 33. To do. As shown in FIG. 4, the display processing unit 55 displays an image acquired from the database 22 in the image display area 400 of the display unit 36. In the following, an example in which it is assumed that the image I1 to the image IN have substantially the same image range (from the top of the cervical vertebra to the lowest point of the sciatic bone) will be described. However, the range of this image does not need to be the same for the images I1 to IN, and the image range may be different. Note that the process of displaying the image acquired from the database on the display unit 36 by the display processing unit 55 may not be executed. In step S101, when the image acquisition unit 51 does not acquire a plurality of images (that is, when only one image is acquired), the processing after step S103 is not executed. Good. That is, when the image acquisition unit 51 acquires a plurality of images, the processing after step S103 may be executed. That is, this process corresponds to an example of a section specifying unit that specifies a section to which a cross section corresponding to a slice image belongs when a plurality of images are acquired.

(Step S103: Identification of dependent section for each slice of each image)
In step S103, for each image from the images I1 to IN, the section identification unit 52 determines in which section the slice is included among the plurality of sections included in the section definition information for each slice constituting the image. Whether it is subordinate or not is identified and stored in the RAM 33.

  Here, as shown in FIG. 5, consider an example in which an image I1 is composed of 181 slices representing the vertebral apex to the lowest point of the sciatica. In the example of FIG. 5, a coordinate system Z is defined with the top position of the image I1 as the origin, the topmost slice in the image I1 is z = 0, the next slice is z = 1, and the bottom is the lowest in the image I1. A coordinate system in which the slice located is z = 180 is shown. Hereinafter, in this paper, when a certain coordinate system (axis) is expressed, it is expressed using a capital letter (Z), and when expressing a coordinate value of a certain coordinate system, it is expressed using a small letter (z). To do. In the coordinate system represented by Z, since the coordinate value (z) increases in units of slices with the top slice constituting the image as the origin, Z depends on the imaging range and the size of the body (bone) of the subject. It is a coordinate system in which coordinate values on the axis can change. That is, in this coordinate system, the positions of the slice images indicated by z = 0 in different three-dimensional images are not necessarily anatomically substantially the same position. In the example of FIG. 5, the positions of the vertex of the cervical vertebra, the vertex of the thoracic vertebra, the vertex of the lumbar vertebra, the vertex of the sacrum, and the lowest point of the sciatus in the image I1 are z = 0, 23, 96, 140, and 180, respectively. It shall be. The process of acquiring the positions of these anatomical feature sections corresponds to an example of a position acquisition unit that acquires position information indicating the position of the anatomical feature section of the subject indicating the boundary of the section.

  In each slice of the image I1, the process of identifying the dependent section of the slice refers to an operation of assigning a predetermined section to all slices from z = 0 to z = 180. In the example of FIG. 5, it is ideal to assign “section 2” to slices in the range of z = [0, 23]. Similarly, it is desirable to assign “section 3” when z = (23, 96], “section 4” when z = (96, 140], and “section 5” when z = (140, 180].

  As a method for automatically and suitably performing the identification processing of the dependent section, a method using a matching process based on an existing image feature amount for each slice may be considered. When this method is taken, before performing the identification process, the data of the image feature quantity that can be calculated from a certain slice and the data that associates the information of the predetermined section corresponding to the slice as one data set, It is necessary to prepare a database that collects them. In addition, the information on the image feature amount can be information on pixel values constituting the slice and information on output values of various image filters applicable to the slice. In a more specific example, when capturing a feature of a bone shown in a CT image, the volume of a pixel value equal to or greater than a certain threshold (for example, 150 [HU]) is utilized using the fact that the bone region has a high pixel value. Can be used as a feature amount. Or the histogram of the pixel value contained in an image can also be used as a feature-value.

  In the following, an example of a method for performing identification processing of dependent sections in each slice using matching of feature quantities with a data set included in a database when an unknown three-dimensional image is input will be described. A method for realizing this matching is as follows. First, a similarity is calculated between feature quantity information that can be calculated from a slice of an input image and feature quantity information that is stored in advance in a database. Then, by selecting the data set having the highest similarity among the calculated results from all the data sets, matching between the input image and the database image can be realized. The identification of the dependent section of the current processing target slice can be realized by assigning information on a predetermined section linked to the data set having the highest similarity obtained by the matching as information on the dependent section of the slice. Then, by performing the same process for all slices constituting the three-dimensional image, it is possible to automatically identify the section in which each slice is dependent. Note that not only the data set with the highest similarity but also a predetermined number of data sets are selected from the one with the highest similarity, and the section assignment is performed based on the result of integrating the information of the dependent sections by a method such as majority vote. Also good.

  Moreover, you may give the restriction | limiting based on the characteristic of the anatomical structure of a human body about the process which allocates the information of said dependent area. For example, when the section definition information shown in FIG. 3 is used, no other section should enter between the section 1 and the section 2 as prior knowledge (for example, between the top of the head and the thoracic vertebrae). There is no lumbar spine). Using this fact, when allocating dependent section information in order from the upper (or lower) slice of the input three-dimensional image, the dependent section of the slice located above (or below) the slice to be processed Based on the information, the number of data sets used for matching may be limited. That is, it is possible to prevent inappropriate assignment by excluding from the database a data set associated with information that is not appropriate as an estimation result. In addition, the calculation time can be shortened by limiting the number of data sets to be matched.

  Although an example in which the dependent section is identified in units of slices based on the feature amount calculated in units of slices is shown here, the processing target may be a three-dimensional image configured with a plurality of slices. Even in this case, the similarity between the input image and the image of the data set may be calculated using information on the feature amount that can be calculated from a plurality of slices. The identification of the dependent section may be performed only for one slice (for example, the central slice of the three-dimensional image) included in the three-dimensional image, and all the slices of the three-dimensional image may be assigned. An assignment may be made to the assignment.

  Moreover, the identification method of a dependent section in the above-mentioned slice unit shows one example, and an equivalent process may be performed using another alternative means. For example, the position of the anatomical feature cross section corresponding to the boundary of the dependent section is estimated by the same matching process, and the corresponding section is assigned between a plurality of anatomical feature cross sections to identify the dependent section. May be. This process corresponds to an example of a position acquisition unit that acquires position information indicating a position in an image of an anatomical feature section in which the boundary of the anatomical structure is depicted. Further, this process corresponds to an example of a section specifying unit that specifies a section to which a cross section corresponding to a slice image belongs based on the acquired position information of the anatomical feature cross section.

  Finally, for each image from the images I1 to IN, the section identification unit 52 searches for a slice corresponding to the boundary of the section for each dependent section based on the identified dependent section information, and stores the result in the RAM 33. Store.

(Step S104: Calculation of coordinate values in the reference coordinate system corresponding to the dependent section of each slice of each image)
In step S104, the coordinate calculation unit 53 calculates the coordinate value of the slice in the reference coordinate system corresponding to the dependent section, based on the information of the dependent section in each slice identified in step S103, and stores it in the RAM 33. . This calculation is performed for each image from I1 to IN.

  Here, FIG. 6 shows an example in which the reference coordinate system Z ′ is defined for each section with respect to the section definition information shown in FIG. 3. Here, the minimum value of the reference coordinate system Z ′ in a certain section n is expressed as z′n_min, and the maximum value is expressed as z′n_max. In the reference coordinate system Z ′ shown in FIG. 6, the coordinate value of the slice corresponding to the top of the head is defined as z ′ = 0, and the coordinate value of the slice corresponding to the vertex of the cervical vertebra is defined as z ′ = 100. That is, in the case of section 1, the minimum value z′1_min = 0 and z′1_max = 100. Here, by setting the minimum value z′2_min = z′1_max in the section 2, the coordinate value at the boundary between the section 1 and the section 2 can be continuously obtained. In addition, the anatomical feature cross section below the apex of the cervical vertebra has a coordinate system in which the coordinate value of the position of the cross section increases by 100 each time the feature cross section becomes one lower cross section. The coordinate value of the slice corresponding to the bottom is set to z ′ = 700. Here, based on the example shown in FIG. 5, FIG. 7 shows an example in which the coordinate system Z having the top position of the image I1 as the origin is associated with the reference coordinate system Z ′. In FIG. 7, the position of z = 0 corresponds to z ′ = 100, and the position of z = 23 corresponds to z ′ = 200.

The association of the position between the coordinate system Z and the reference coordinate system Z ′ shown in FIG. 7 can be realized by the following processing. That is, first, the coordinate calculation unit 53 associates, for each dependent section, the position of a slice that hits the boundary of the section and the position of the boundary of the section in the reference coordinate system. Next, positions other than the boundaries are associated. Here, regarding the coordinate system Z and the reference coordinate system Z ′, if the same dependent section, the anatomical structures included in the section are arranged with a balance of substantially the same size regardless of the scale. I reckon. Therefore, the coordinates are linearly associated with each dependent section. Specifically, it is considered that the slice included in the range of the coordinate z = [0, 23] corresponding to the section 1 linearly increases in coordinate value, and the coordinate value in the reference coordinate system Z ′ is set to be lower, for example. What is necessary is just to calculate with a formula. The following f (z) represents the coordinate value of the reference coordinate system Z ′ corresponding to the coordinate value z of the slice.
f (z) = z′n_min + (z−zn_min) / (zn_max−zn_min) × (z′n_max−z′n_min) (1)

  In Expression (1), zn_min is the minimum value of the coordinate value z of the slice in the section n, and zn_max is the maximum value. As a specific example, when f (z) is calculated on the assumption that “section 2” is assigned to the position of z = 10 in FIG. 7, the respective parameters are z2_min = 0, z2_max = 23, z '2_min = 100, z'2_max = 200. In addition, with respect to the other dependent sections, similarly, by substituting the minimum and maximum values of the slices of each dependent section into the respective parameters, the calculation is performed for each dependent section by the calculation of Equation (1). Coordinate values in the coordinate system can be calculated. Note that the method of associating positions other than the boundaries is not limited to this, and may be associated non-linearly.

  Here, the effect of providing the reference coordinate system Z ′ will be described. The effect of setting the reference coordinate system Z ′ is that even if different three-dimensional images are used, the anatomically characteristic cross-section defined by the section definition information has a substantially identical anatomical location as the reference coordinate system. Z ′ is at a point having the same coordinate value. By using the normalized space information, there is an effect that the process of displaying the positions of different three-dimensional images in the subsequent stage together becomes easy.

  Further, when it is desired to further strengthen the spatial normalization, normalization in finer units can be realized by defining the section definition information so that the anatomical structure of the human body is divided into smaller sections. For example, let us consider a case where there are only two anatomical feature cross sections included in the section definition information: the top of the head and the bottom of the foot. At this time, in the image to be aligned, in the reference coordinate system Z ′, the space is normalized so that the top of the head has the minimum value and the bottom of the foot has the maximum value. However, when considering between the anatomical feature sections, the further away from those anatomical feature sections, the weaker the normalization effect. This is because there are individual differences in the size of the anatomical structure of the human body, and the balance of the size of the anatomical structure existing between the cross sections of the anatomical features (the top of the head and the bottom of the foot) is different for each person. is there. In other words, if the balance of the size of the anatomical structure included in the images differs between the images to be aligned, even slices that have the same coordinate value in the reference coordinate system will be placed in positions corresponding to the anatomies. It may not be possible. This difference in balance can be roughly caused by a difference in age or the like, but can be slightly caused by individual variation. Increasing the number of anatomical feature cross sections included in the section definition information is divided into a plurality of sections at anatomically meaningful positions in the body axis direction, and a reference coordinate system (normalized space provided for each section). ) Is equivalent to normalizing the image. It is considered that normalizing by subdividing the space has the effect of applying normalization that absorbs the difference in the balance of the size of the anatomical structure in finer units.

(Step S105: Image display based on coordinate values in the reference coordinate system)
In step S105, the alignment unit 54 identifies slices having the same or substantially the same coordinate value in the reference coordinate system Z ′ for the images from the images I1 to IN based on the coordinate values calculated in step S104. Then, the display processing unit 55 displays the images of the identified slices in the image display area 400 of the display unit 36. In other words, the display processing unit 55 corresponds to an example of a display control unit that causes the display unit to display each of the associated slice images in a comparable form. Note that the corresponding slice image may be displayed using any known method such as parallel, superimposition, or time division.

  For example, consider a case where the user operates the image I1 within the image display area of the display unit 36. At this time, when the user instructs display of a certain slice of the image I1 (hereinafter referred to as the target slice), the alignment unit 54 determines the coordinate value (in the reference coordinate system Z ′) of the target slice from the result calculated in step S104. ZI1 ′) is acquired. Then, for each image from the image I2 to the image IN, the coordinate value of each slice in the reference coordinate system Z ′ is referred to, and the slice having the coordinate value closest to ZI1 ′ is identified as the corresponding slice of the target slice. . Thereafter, the display processing unit 55 displays the identified slice in the image display area of the display unit 36. According to this processing, one noticeable slice of the image I1 displayed by the user operation and a slice (corresponding slice) including substantially the same anatomical structure as the noticeable slice in the image I2 to the image IN are presented to the user. it can.

  Note that the processing for displaying the slice at the corresponding position does not need to be performed for all the images I1 to IN, and may be performed only for the image selected by the user via the operation unit 35.

  Further, as described above, the effect of normalization described above becomes weaker as the slice image indicating the cross section existing between the anatomical characteristic cross sections becomes farther from the anatomical characteristic cross section. Therefore, the display processing unit 55 may change the display mode of the slice image according to the distance between the cross section indicated by the slice image to be displayed and the anatomical feature cross section. For example, the display processing unit 55 sets the color of the edge of the slice image to red when the distance between the cross section indicated by the slice image to be displayed and the closest anatomical feature cross section is larger than a predetermined distance. To do. The display processing unit 55 sets the color of the edge of the slice image to blue when the distance between the cross section indicated by the slice image to be displayed and the closest anatomical feature cross section is not more than a predetermined distance. To do. Thus, by changing the display mode of the slice image according to whether or not the distance between the cross section indicated by the slice image to be displayed and the closest anatomical feature cross section is larger than a predetermined distance, The user can grasp the reliability of normalization. Note that changing the display mode includes not only changing the color but also changing the display mode of characters and symbols. Further, information on the distance from the nearest anatomical feature cross section may be directly displayed without going through threshold processing based on a predetermined distance. At this time, the distance may be the number of slices, a physical distance (how many mm), or a distance in the reference coordinate system Z ′. Further, the pseudo color assigned according to the distance may be set as the edge color of the slice image.

  According to this embodiment, since the difference in the balance of the size of the anatomical structure can be absorbed by normalizing the position of the anatomical structure of the human body for each part in the body axis direction, in the alignment process between different images, There is an effect that a highly accurate alignment result can be provided to the user.

(Modification 1)
In the above-described embodiment, when the user displays and observes (diagnosis) a plurality of three-dimensional images, an example in which the process of calculating the coordinate value of each slice in the reference coordinate system Z ′ is performed for each image is shown. . However, the procedure for performing the process of calculating the reference coordinate value is not necessarily limited to this. For example, the image processing apparatus 10 performs the processing from step S101 to S104, and calculates the coordinate value in the reference coordinate system Z ′ for each slice of the images I1 to IN. Then, the calculated result may be attached to each image as auxiliary information of the slice and stored in the database 22. At this time, it is not necessary to display an image in the process of step S102. Further, it is not necessary to simultaneously process a plurality of images to be compared, and each image may be processed individually. In this case, this process can be executed prior to the observation (diagnosis) of the image by the doctor, for example, immediately after each image is captured. That is, this procedure corresponds to executing the processing from step S103 to step S104 in advance for each image.

  And when a user observes (diagnosis) of an image, it can be set as the structure which the image processing apparatus 10 performs the process of step S102 and step S105. In the case of taking this procedure, when the image acquisition unit 51 acquires an image in step S102, a process for reading the reference coordinate value of each slice stored as incidental information of the image is added. Processing equivalent to the embodiment can be realized.

  By the processing of the first modification, it is possible to omit the calculation of the coordinate value in the reference coordinate system Z ′ when displaying the image necessary in the first embodiment, so that the user can observe (diagnose) the image. The waiting time when performing can be shortened.

  Note that it is determined whether or not the acquired image holds the reference coordinate value of each slice as supplementary information, and the processing of step S103 and step S104 is performed on the image only when the supplementary information is not included. You may make it perform. According to this, it is possible to minimize the waiting time in the case where the processed image and the unprocessed image are mixed in the calculation of the reference coordinate value.

(Modification 2)
In the first embodiment, the alignment unit 54 performs calculation for identifying a corresponding slice of the target slice, triggered by the user specifying a target slice in the three-dimensional image in step S105. However, this procedure is not necessarily limited to this method. For example, in the process of step S105, the alignment unit 54 may identify the correspondence between the slices of all the images I1 to IN through the reference coordinate system Z ′ in advance. Good. Thereby, the identifier (slice number) of the corresponding slice in another image can be held as supplementary information in each slice of each image. When the user designates a target slice, the display processing unit 55 may read the identifier of the corresponding slice held by the target slice and display the image of the corresponding slice together with the image of the target slice. . Thereby, since the calculation of identification of the corresponding slice when displaying the image required in the first embodiment and the modification 1 can be omitted, when the user instructs display of a slice of an image , Instantly display corresponding slices of other images.

(Other embodiments)
The present invention supplies a program that realizes one or more functions of the above-described embodiments to a system or apparatus via a network or a storage medium, and one or more processors in a computer of the system or apparatus read and execute the program This process can be realized. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

<Second Embodiment>
In the first embodiment, in step S103, the section identifying unit 52 identifies, for each slice constituting the images I1 to IN, which section the slice belongs to among the plurality of sections included in the section definition information. doing. Thereafter, in step S104, the coordinate calculation unit 53 searches for the position that becomes the boundary of the section in each dependent section, and associates it with the position of the boundary of the reference coordinate system corresponding to the section definition information, thereby performing spatial normalization. It was realized. However, another method may be used for realizing the above spatial normalization.

  In the second embodiment, for the 3D image acquired by the image acquisition unit 51, the position of the anatomical feature cross section included in the section definition information is directly estimated, and the position of the cross section is acquired. An example of a method for performing spatial normalization using the result will be described. In this embodiment, since processing based on the information of the entire three-dimensional image can be performed, there is an effect that matching can be performed with higher accuracy than the matching processing in units of slices described in the first embodiment.

  The configuration of the image processing apparatus according to the second embodiment is the same as that of the first embodiment, but the following function is added to the section identification unit 52.

  In the second embodiment, the section identifying unit 52 uses the cross-section position estimation process for the images I1 to IN to be aligned, and uses the cross-section position estimation process to determine the anatomical feature cross section included in the section definition information. Get the position of.

  Here, a second example of the processing flow of the image processing apparatus 10 will be described with reference to FIG.

  FIG. 8 is a flowchart illustrating an example of a process for displaying a plurality of images whose positions are aligned in a certain reference coordinate system, starting from the section definition information acquisition process in the image processing apparatus 10. In the steps shown in this flowchart, steps S101 to S102 and steps S104 to S105 perform the same processes as steps S101 to S102 and steps S104 to S105 in the first embodiment shown in FIG. That is, the process different from the first embodiment is step S203. Only the difference between the added process and the first embodiment will be described below.

(Step S203: Acquisition of the position of the anatomical feature cross section)
In step S203, the section identification unit 52 estimates the position of the anatomical feature cross section included in the section definition information from the image for the three-dimensional image (image I1 to image IN) acquired by the image acquisition unit 51. And stored in the RAM 33.

  As a method for realizing the above, a method using an existing alignment technique for a three-dimensional image can be considered. Even when the position of the anatomical feature cross section is estimated using this alignment technique, the preparation of a certain database is necessary as in the first embodiment. In the second embodiment, it is assumed that there is data associating a three-dimensional image (reference image) and position information of a predetermined anatomical feature section as a data set. When the position of the anatomical feature cross section is estimated from the image by the alignment process, it is desirable that the range of the reference image in the database is wider than the input three-dimensional image. This is because if the range of the image in the database is narrower, a cross section corresponding to the anatomical feature cross section originally included in the input three-dimensional image may not exist in the image. This is because the estimation of the feature cross section may fail.

  In the method using the above alignment technique, the section identification unit 52 performs a process of inputting each image of the input three-dimensional image data (image I1 to image IN) and an image (reference image) of a data set included in the database. Perform 3D image alignment. That is, this process corresponds to an example of an alignment unit that performs alignment between the acquired image and a three-dimensional reference image including a slice image associated with information on the section. However, it is sufficient for the alignment here to be able to perform alignment processing of each image from the image I1 to the image IN and the reference image only for the axis in which the anatomical feature section is defined. This alignment processing in one axial direction can be realized by, for example, optimization processing that maximizes the degree of similarity between the alignment target images for the processing target axis. For the similarity between images, the sum of the absolute values or sums of squares of the differences between the image density values and the feature amounts can be used, but other indices may be used.

  A method for estimating the position of an anatomical feature cross section for a certain image I1 using the database is as follows. The section identification unit 52 first performs the above alignment process on the image I1 and the images of all data sets. By performing this alignment processing, the shift amount between images and the similarity between images can be calculated for the combination of the image I1 and the images of all the data sets. Here, the shift amount between images (Δz) represents the difference between the input image and the image of the data set when the coordinate values of the corresponding cross-sectional positions are zTest and zRef, respectively (Δz = zTest). -ZRef). These coordinate values are coordinate values when the position of the top slice of each image data is considered as the origin.

  Subsequently, the section identification unit 52 selects a data set most similar to the input image from among the maximum similarity values between images calculated for all data sets.

  Finally, the section identification unit 52 obtains the position information of the anatomical feature slice in the input image based on the position information of the predetermined anatomical feature slice linked to the image (reference image) of the selected data set. presume. That is, this process is an example of a section specifying unit that estimates a section to which a cross section corresponding to a slice image included in the image belongs based on information regarding a section associated with the slice image included in the aligned reference image. It corresponds to.

  Regarding the estimation processing of the position information of the anatomical feature cross section, since the shift amount (Δz) between the input image and the selected reference image can be calculated by the processing so far, this information may be used. Specifically, by calculating the sum of the position information of the predetermined anatomical feature cross section linked to the selected reference image and the amount of deviation (Δz), the anatomical feature cross section included in the input image is calculated. The position can be estimated.

  In the above example, only the data having the highest similarity is used. However, the estimation method is not limited to this. For example, based on the result of selecting a predetermined number of data in descending order of similarity, the position may be estimated using an average value thereof.

  Note that a dependent section defined by the section definition information may be assigned to a slice existing between a plurality of anatomical feature cross sections included in the input image. However, when the position of the anatomical feature cross section can be estimated as in the present embodiment, the process of estimating the dependent section in each slice described in the first embodiment is unnecessary. The reason is that the calculation by the coordinate calculation unit 53 in step S104 is that the anatomical feature cross-section position information included in the input image and the reference coordinate system corresponding to each section included in the section definition information are defined. This is because it can be implemented.

  According to the present embodiment, since processing based on the information of the entire three-dimensional image can be performed, the matching can be performed with higher accuracy than the matching processing in units of slices described in the first embodiment.

<Third Embodiment>
In the second embodiment, in step S203, the section identification unit 52 performs alignment with the images included in the database for the three-dimensional image (image I1 to image IN) acquired by the image acquisition unit 51. To estimate the position of the anatomical feature cross section. In the subsequent step S104, the coordinate value in the reference coordinate system Z ′ is calculated based on the estimated position of the anatomical feature cross section.

  The configuration of the image processing apparatus according to the third embodiment is the same as that of the second embodiment, but the following function is added to the section identification unit 52.

  In the third embodiment, the section identifying unit 52 uses the cross-section position estimation process for the images I1 to IN to be aligned, and uses the cross-section position estimation process to determine the anatomical feature cross-section included in the section definition information. Get the position of. The basic functions are the same as in the second embodiment, but the differences from the second embodiment are as follows. In the third embodiment, the function of estimating not only the range of the input image but also the position of the anatomical feature cross section outside the range and calculating the coordinate value in the reference coordinate system Z ′ based on the estimation result Has been added. This function is a position acquisition unit that acquires position information indicating the position of an anatomical feature cross section outside the imaging range by estimating the position of the anatomical feature cross section outside the imaging range of the subject included in the image. It corresponds to an example. Thereby, it is possible to estimate the position of the anatomical feature cross section that exists outside the image range with respect to the part that is interrupted at the upper and lower ends in the body axis direction of the image. That is, even if there is a slice that is not sandwiched between anatomical feature sections within the range of the image, it is possible to associate the slice with coordinates of a reference coordinate system with higher accuracy. Only the difference between the added process and the second embodiment will be described below.

  According to the method of the first embodiment and the second embodiment, the coordinate values in the reference coordinate system Z ′ can be calculated for slices existing between the estimated plurality of anatomical feature cross sections. That is, when the positions of the plurality of anatomical feature cross sections correspond to the positions of the image edges of the input image (image A1) as in the image A1 shown in FIG. 9, all slices constituting the image A1 are used. The coordinate value in the reference coordinate system Z ′ can be calculated. However, when considering a case where the image range of the input image is the image A2, the image A3, and the image A4 shown in FIG. 9, with respect to some or all of the slices constituting the input image, the reference coordinate system Z ′ The coordinate value cannot be calculated. The reason is that if only a plurality of anatomical feature sections included in the range of the image are used in these situations, the slices constituting the image A2, the image A3, and the image A4 are partially or all slices. Is not present between the anatomical feature sections. That is, since the parameters, zn_max and / or zn_min used in the calculation (equation (1)) of the coordinate calculation unit 53 in step S104 cannot be acquired, the coordinate value in the reference coordinate system Z ′ can be calculated for the slice. Can not.

  In order to solve this problem, in step S203, the section identification unit 52 of the present embodiment estimates the position of the anatomical feature cross section within the range of the input image as in the second embodiment, and further The position of the anatomical feature cross section outside the range of the image is estimated. Even when the anatomical feature cross section is estimated outside the range of the input image, the coordinate calculation unit 53 in step S104 uses the calculation method described in the second embodiment, so that each slice constituting the input image The coordinate value in the reference coordinate system can be calculated. In addition, when the section included in the input image corresponds to the end section (the top of the head or the bottom of the foot in FIG. 3) in the section definition information, it is not necessary to estimate a section outside the sections.

  As a method for estimating the anatomical feature cross section outside the range of the input image, a method similar to the method based on the alignment technique described in the second embodiment can be used. However, when calculating the position of the anatomical feature cross section in the input image using the position of the anatomical feature cross section associated with the image of the data set, it is necessary to extend the calculation range. That is, instead of calculating the position of the anatomical feature cross section only from the imaging range of the input image, it is necessary to calculate the position of the cross section for coordinates outside the imaging range of the input image. In order to realize the calculation of the position of the cross section, it is necessary to provide a constraint that only a data set satisfying a certain condition is used for processing when selecting a data set from the database.

  The above condition is the state after the alignment with the input image, the input image and a part of the image of the data set are common, and the anatomical feature cross section outside the range of the input image Refers to being linked to a data set.

  Here, the following two patterns are conceivable for the data set that satisfies the above conditions. The first pattern is a case where a plurality of anatomical feature sections linked to the data set include the range of the input image (exist so as to be sandwiched) (pattern 1). The following second pattern is a case where either the upper or lower cross section is associated with the data set with respect to the anatomical feature cross section outside the range of the input image (pattern 2).

  Hereinafter, as an example of a specific flow of the estimation method of the anatomical feature cross section outside the range of the input image, an example of the estimation method when using the data patterns of the two patterns will be described.

(Estimation method when pattern 1 data set (group) is used)
First, the processing is performed in the same manner as the flow described in the second embodiment until the registration processing of the input image and the image on the database. Then, when selecting a data set that shows the maximum value of similarity between images after the alignment process, the selection is made only from the data set that becomes the pattern 1. Finally, the positions of the cross sections in the coordinate system Z of the input image are estimated for all the positions of the anatomical feature cross sections associated with the image of the selected data set, including those outside the range of the input image. Here, the selection of the data set is not limited to the maximum value of similarity, and a predetermined number may be selected in descending order of similarity. When multiple data sets are selected, the input image can be calculated by calculating the average (or maximum / minimum, etc.) of the coordinate values of the same section for the anatomical feature sections linked to each data set. The position of the cross section in the coordinate system Z may be estimated.

  Here, whether or not a certain data set is the pattern 1 can be determined by the following method. First, among the plurality of anatomical feature cross sections included in the data set, the position of the uppermost cross section and the position of the lowermost cross section are identified. Then, whether or not the pattern 1 is the case can be determined by checking whether the image range of the input image is within the range between the cross sections. As a specific example, consider a situation in which an image A3 shown in FIG. 9 is input data, and images A1 and A2 are images of a data set included in a database. Here, in the figure, it is assumed that the alignment of the images A1, A2, and A3 has been completed.

  First, considering the input image A3 and the image A1 included in the database, the anatomical feature cross sections included in the image A1 are the cross section 3, the cross section 4, the cross section 5, and the cross section 6. Further, among these cross sections, the uppermost cross section is the cross section 3, and the lowest cross section is the cross section 6. Here, when the range of the image A3 is seen, it can be seen that the range of the image A3 is included in the range between the cross section 3 and the cross section 6. That is, it can be determined that the image A1 included in the database satisfies the condition for estimating the position of the anatomical feature cross section outside the range of the input image A3, and matches the case of the pattern 1.

  Next, consider the input image A3 and the image A2 included in the database. The anatomical feature cross sections included in the image A2 are the cross section 4, the cross section 5, and the cross section 6. Among these cross sections, the uppermost cross section is the cross section 4, and the lowest cross section is the cross section 6. When the range of the image A3 is viewed in the same manner as described above, it can be seen that a part of the range of the image A3 is not included in the range between the cross section 4 and the cross section 6. That is, the image A2 included in the database satisfies the condition for estimating the position of the anatomical feature cross section outside the range of the input image A3, but does not match in the case of the pattern 1 (the pattern 2). Can be determined.

  Finally, FIG. 10 shows a method for estimating the position of the cross section in the coordinate system Z of the input image, including outside the range of the input image, based on the position of the anatomical feature cross section associated with the image on the database. Use to supplement. In the example shown in FIG. 10, it is assumed that the image A3 is input data, the image A1 is an image on the database, and the alignment of the images A1 and A3 is completed. In FIG. 10, a coordinate system ZRef whose origin is the highest position of the image A1 and a coordinate system ZTest whose origin is the highest position of the image A3 are defined. Now, by aligning the images A1 and A3, the shift amount Δz (= zTest−zRef) for calculating the corresponding positions in these coordinate systems is −17. Further, in the image A1 on the database, information on the characteristic cross sections of the cross section 3, the cross section 4, and the cross section 5 is associated with zRef = 5, 46, and 70, respectively. Here, when the position of the cross section 3 (zRef = 5) is considered in the coordinate system ZTest, zTest = 5 + (− 17) = − 12 by zTest = zRef + Δz. The same calculation is possible for the cross section 4, and the position of the cross section 4 is zTest = 29. Now, since the image range of the image A3 is zTest = [0, 26], it can be seen that the position of the anatomical feature cross section outside the range of the image A3 can be estimated.

  Note that the above estimation method shows one example, and the position of the anatomical feature cross section outside the image range may be estimated by another method. For example, as another method of estimating an anatomical feature cross section outside the range of an input image, there is a method of performing estimation processing by combining two or more data sets on a database as shown below. Conceivable.

(Estimation method when pattern 2 data set group is used)
In the estimation method using the data set satisfying the pattern 1, an example in which a data set having both upper and lower cross sections is used for the anatomical feature cross section outside the range of the input image is shown. However, when two or more data sets are used in combination, the position of the anatomical feature cross-section outside the range of the input image can be estimated by using a database including a plurality of data sets to be the pattern 2. it can.

  Also in the estimation method using the data set group to be the pattern 2, the processing is performed in the same manner as the flow described in the second embodiment until the registration processing of the input image and the image on the database. However, there is a difference in selecting a plurality of data sets from the data set group to be the pattern 2 when selecting a data set based on the similarity between images after the alignment process. In the estimation using the data set group of pattern 2, the selection of the plurality of data sets is performed with respect to the anatomical feature cross section outside the range of the input image, at least one data for each of the upper side and the lower side of the range. Select so that the set is included. Finally, based on the positions of all the anatomical feature cross sections associated with the selected images of the plurality of data sets, the position of the cross section in the coordinate system Z of the input image is estimated including outside the range of the input image. .

  When calculating the position of the anatomical feature cross section outside the range of the input image, a plurality of data sets may be used in the upper or lower calculation. Specifically, when performing an estimation process using anatomical feature sections associated with multiple data sets, the average (or maximum / minimum, etc.) of the coordinate values of the same section between the data sets is used. The position of the cross section in the coordinate system Z of the input image can be estimated from a plurality of data sets.

  As a specific example, consider a situation in which an image A3 shown in FIG. 9 is input data, and an image A2 and an image A4 are images of a data set included in a database. Here, in the figure, it is assumed that the alignment of the images A2, A3, and A4 has been completed.

  Considering the input image A3 and the image A2, the anatomical feature cross sections included in the image A2 are the cross section 4, the cross section 5, and the cross section 6. Looking at the range of the image A3, it can be seen that the cross section 4 is outside the range of the image A3 and is a lower anatomical feature cross section. Similarly, considering the input image A3 and the image A4, the anatomical feature cross sections included in the image A4 are the cross section 2 and the cross section 3. The section 3 is outside the range of the image A3 and is an upper anatomical feature section. That is, the image A2 and the image A4 satisfy the condition for estimating the position of the anatomical feature cross section outside the range of the image A3, and match the case of the pattern 2. Then, by performing estimation processing by combining these data sets (image A2 and image A4) as described above, both the upper and lower cross sections with respect to the anatomical feature cross section outside the range of the input image. The position can be estimated. That is, for the upper side outside the range of the image A3, the position of the anatomical feature cross section can be estimated by using information on the anatomical feature cross section (the cross section 3 and the cross section 2) of the image A4. Further, for the lower side outside the range of the image A3, the position of the anatomical feature cross section can be estimated by using the information of the anatomical feature cross section (the cross section 4, the cross section 5, and the cross section 6) of the image A2.

  Through the above processing, information on the anatomical feature cross section outside the range of the input image necessary for calculating the coordinate value in the reference coordinate system Z ′ can be acquired.

  Even when the position of the anatomical feature cross section outside the range of the image is obtained by the estimation method shown so far, the coordinate calculation unit 53 uses the reference coordinate system in the method described in the second embodiment in step S104. A coordinate value in Z ′ can be calculated. Therefore, according to the method of the present embodiment, the coordinate values in the reference coordinate system Z ′ can be calculated for all slices constituting the input image.

  According to the present embodiment, it is possible to estimate the position of the anatomical feature cross section that exists outside the image range with respect to the part that is interrupted at the upper and lower ends in the body axis direction of the image. That is, even when there is a slice that is not sandwiched between anatomical feature sections within the range of the image, it is possible to associate with the coordinates of the reference coordinate system with higher accuracy.

<Fourth Embodiment>
The method of associating slices between a plurality of three-dimensional images shown in the first embodiment is a method of calculating coordinate values in the reference coordinate system for each slice of each image and associating those slices. In this method, since only one coordinate value can be calculated for one slice, it is not possible to perform an appropriate association when it is necessary to assign a plurality of coordinates despite the same slice. There is. Specific examples of this problem include anatomical structures such as bones and lungs that are in a sliding relationship. First, consider a case where the upper and lower ends of the lung are included in the anatomical feature section of the section definition information. According to the technique described in the first embodiment, with respect to a cross section (slice) representing the anatomical information, it can be expected that substantially the same coordinate value is given in the reference coordinate system even for different three-dimensional images. . However, considering the vertical movement of the lower end of the lung, the vertebral body does not completely follow the movement of the lung (slip occurs), so when looking at the relative position from the position of a certain vertebral body, the slice at the lower end of the lung The relative position of may vary depending on the patient's respiration rate at the time of imaging. Therefore, slices that show the same coordinate value in the space normalized at the upper and lower ends of the lungs have approximately the same anatomical position in terms of the anatomical structure of the lungs, but in different positions from the bone perspective. There is a possibility. That is, for example, when the anatomical feature cross section of the section definition information is based only on the anatomical information about the lung even though the user is paying attention to the bone, the association of slices between different images is inappropriate. Is likely to be.

  In the fourth embodiment, a plurality of section definition information corresponding to different parts is prepared in advance, the section definition information suitable for the part that the user is paying attention to is selectively used, spatial normalization, and An example of a method for associating slices will be described. According to the present embodiment, a plurality of corresponding slices can be calculated for one slice, and the corresponding slices can be switched according to the part that the user pays attention to, so that there is an effect that higher-level association can be performed. .

  The configuration of the image processing apparatus according to the fourth embodiment is the same as that of the first embodiment, but the following functions are added to the section definition information acquisition unit 50 and the alignment unit 54.

  In the fourth embodiment, the section definition information acquisition unit 50 reads a plurality of predefined section definition information. Further, the alignment unit 54 has a function of selectively switching the corresponding slices based on the part that the user is paying attention to. The plurality of section definition information is preferably information based on anatomical structures such as bones and lungs in a slipping relationship. In the following example, an example is described in which section definition information based on an anatomical structure related to bone (section definition information related to a bone) and section definition information based on an anatomical structure related to a lung (section definition information related to a lung) are used. Do.

  The processing procedure of the image processing apparatus 10 in the fourth embodiment is the same as the processing procedure of the image processing apparatus 10 in the first embodiment shown in FIG. However, processing related to considering a plurality of section definition information is added / changed in each step. Only the difference between the added process and the first embodiment will be described below.

(Step S101)
In step S <b> 101, when the user instructs the start of image processing via the operation unit 35, the section definition information acquisition unit 50 acquires two or more predefined section definition information from the database 22 and stores them in the RAM 33. . For example, in the example of the present embodiment, section definition information about bones and section definition information about lungs are acquired. That is, a plurality of section definition information based on different anatomical structures is acquired.

  For the subsequent steps S102 to S104, the same processing as in the first embodiment is performed. However, for steps S103 and S104, processing is performed for each of the plurality of section definition information acquired in step S101.

(Step S103)
In step S103, the section identifying unit 52 identifies the dependent section based on the respective section definition information for each slice of each image. For example, in the example of the present embodiment, the dependent section related to the bone is identified based on the section definition information related to the bone, and the dependent section related to the lung is further identified based on the section definition information related to the lung. The content of this process is the same as that of the first embodiment except that there are a plurality of section definition information.

(Step S104)
In step S104, the coordinate calculation unit 53 calculates coordinate values in the reference coordinate system Z ′ based on the information on the plurality of dependent sections of each slice identified in step S103. For example, in the example of the present embodiment, the coordinates of the reference coordinate system Z1 ′ focused on the bone and the coordinates of the reference coordinate system Z2 ′ focused on the lung are calculated for each slice of each image. The content of this process is the same as that of the first embodiment except that there are a plurality of section definition information.

(Step S105)
In step S105, the alignment unit 54 identifies a corresponding slice (corresponding slice for each target region) of another image defined for each target region with respect to the target slice of a certain image. For example, for each target slice, a corresponding slice focused on the lung and a corresponding slice focused on the bone are identified. This corresponds to an example of an association unit that associates each slice image representing a substantially same position in a plurality of images with respect to each of the plurality of section definition information. Then, when performing the process of displaying the corresponding slice, the display processing unit 55 selectively switches the corresponding slice to be displayed based on the information of the part that the user is paying attention to. That is, when there is information that the user is paying attention to the lung, the attention slice is displayed and the corresponding slice paying attention to the lung is displayed in a form that can be compared. On the other hand, when information indicating that attention is paid to the bone is included, the attention slice is displayed and the corresponding slice attention focused on the bone is displayed in a comparable form. This corresponds to an example of a display control unit that displays the associated slice images on the display unit in a form that can be compared based on information about the selected section definition information.

  This attention site selection can be realized by a method in which the user designates the attention region via the operation unit 35. Alternatively, the selection may be made automatically based on the display conditions of the image displayed on the display unit 36. That is, if the image display parameter is a bone condition (a parameter suitable for bone observation), it is determined that the user is paying attention to the bone. If the display parameter is a lung field condition (a parameter suitable for lung observation), the lung You can determine that you are paying attention to. Specifically, display conditions of the image (image I1) displayed on the display unit 36 in step S102, for example, window parameters for density value conversion (for example, window level (WL) and window width (WW)), etc. Based on the set value, the site of interest is estimated. Thereby, an attention site can be selected automatically. This corresponds to an example of a selection unit that selects one piece of information from a plurality of pieces of information related to the section definition information in accordance with a region that the user pays attention to.

  When a doctor performs an image diagnosis of a CT image, the doctor changes the window level (WL) and window width (WW) according to the part of the subject to which attention is paid, so that the doctor responds to the part of the subject to which the doctor pays attention. The display condition of the image is changed (the density value of the display image is converted). More specifically, for example, when a doctor performs a bone image diagnosis, the window level (WL) is 350 to 500 [H. U. ] And the window width (WW) is 1500-2500 [H. U. ] Value is set. By changing the display condition of the image according to the part of the subject, the part of the subject (for example, bone) that the doctor is paying attention to is displayed as a display image having a density distribution that is easy to see. Therefore, from the set values of the display conditions (for example, the window level (WL) and the window width (WW)) of the display unit 36 on which the image (image I1 to image IN) to be subjected to the alignment process is displayed, It is possible to estimate a part (organ) that is noticed by a doctor who is a user. Note that when a preset value of a display parameter is set and a UI for the user to select it is provided, a site of interest can be assigned in advance for each preset value. In addition, there may be a case where a UI that allows the user to set arbitrary display parameters is provided. In this case, for example, based on the degree of coincidence between the display parameter appropriate for observation of each part and the display parameter set by the user, it is possible to determine which part is the most suitable display parameter. . For example, the overlap ratio between the window range suitable for observing each part and the window range set by the user is obtained, and the determination is made by comparison. Or you may make it determine based on the distance between the window level predetermined as suitable for observation of each site | part, and the window level which the user set. Based on the estimation result, the alignment unit 54 selects segment definition information including an anatomical feature cross section related to the region that the user is paying attention from among a plurality of segment definition information prepared in advance. To switch the corresponding slice.

  As shown in Modification 1 and Modification 2 of the first embodiment, the coordinate values in the reference coordinate system and the information of the corresponding slice in a certain slice are the results calculated in advance. The procedure may be such that the information is stored as supplementary information of the slice and the information is read. In other words, by performing in advance a part of the processing of steps S103 to S105, it is possible to omit the calculation of identification of the corresponding slice when displaying the image, so that it is possible to speed up the display processing of the corresponding slice. .

  Furthermore, in the above example, the coordinate value in each reference coordinate system and the method of calculating the corresponding slice are shown for all of the two or more section definition information. However, the above calculation is performed only for preset information. May be performed. For example, even when three or more section definition information is prepared as a preset, only the frequently used information (lung field condition, mediastinal condition, etc.) may be calculated.

  It should be noted that all the corresponding slices may be displayed side by side without performing selection / determination processing of the region of interest. For example, in the example of the present embodiment, the corresponding slice focused on the bone and the corresponding slice focused on the lung may be displayed side by side with respect to the focused slice of an image. At this time, if a plurality of corresponding slices are substantially the same (the distance in the z direction between the corresponding slices is within a predetermined position), only one of the corresponding slices may be displayed. In addition, when there is section definition information regarding three or more attention parts, a plurality of attention parts may be selected and the corresponding slices may be displayed. For example, a plurality of regions of interest can be assigned in advance for each display parameter preset value. Alternatively, a predetermined number can be selected as a site of interest from sites having higher matching degrees of display parameters. Or you may make it select all the site | parts which satisfy | fill predetermined conditions (For example, the matching degree of a display parameter is more than a threshold value etc.).

  In addition, based on the corresponding slice corresponding to each target region, a corresponding slice when the target region is not identified (default corresponding slice) is calculated, and this is displayed when the target region is not identified. May be. For example, a slice at an intermediate position of the corresponding slice corresponding to each target site may be displayed as a default corresponding slice. Alternatively, any part may be defined as a default part in advance, and a corresponding slice corresponding to the part may be displayed. Moreover, when the site of interest has not been identified, all corresponding slices may be displayed.

  According to the present embodiment, it is possible to switch and display corresponding slices between a plurality of images in accordance with a part to which a user pays attention.

(Modification)
In the fourth embodiment, a dependent section of each slice is obtained for each piece of section definition information corresponding to a plurality of regions of interest, and corresponding slices between images are identified through reference coordinates defined thereby. It was. However, the corresponding slice identification method for associating different slices according to the region of interest is not limited to the method described in the first embodiment, and may be calculated by other alternative means. For example, a configuration may be used in which deformation position alignment between a plurality of three-dimensional images (estimation of a displacement field between images) is performed, and different corresponding slices are set depending on the region of interest based on the result. For example, when attention is paid to a certain organ (for example, bone or lung), a slice that most closely approximates the displacement field is defined as a corresponding slice that focuses on the organ based on the displacement field in the organ region of the attention slice. An identifying method may be used. This also makes it possible to obtain different corresponding slices for each region of interest.

<Other embodiments>
It is also possible to combine at least two of the plurality of modifications described above.

  Further, the disclosed technology can take an embodiment as a system, apparatus, method, program, recording medium (storage medium), or the like. Specifically, the present invention may be applied to a system composed of a plurality of devices (for example, a host computer, an interface device, an imaging device, a web application, etc.), or may be applied to a device composed of one device. good.

  Needless to say, the object of the present invention can be achieved as follows. That is, a recording medium (or storage medium) that records a program code (computer program) of software that implements the functions of the above-described embodiments is supplied to the system or apparatus. Needless to say, such a storage medium is a computer-readable storage medium. Then, the computer (or CPU or MPU) of the system or apparatus reads and executes the program code stored in the recording medium. In this case, the program code itself read from the recording medium realizes the functions of the above-described embodiment, and the recording medium on which the program code is recorded constitutes the present invention.

10 Image processing device 21 Network 22 Database 31 Communication IF
32 ROM
33 RAM
34 storage unit 35 operation unit 36 display unit 37 control unit 50 section definition information acquisition unit 51 image acquisition unit 52 section identification unit 53 coordinate calculation unit 54 alignment unit 55 display processing unit

Claims (15)

Information acquisition means for acquiring section definition information defining a plurality of sections obtained by dividing the human body along the body axis direction;
Image acquisition means for acquiring a three-dimensional image including a plurality of slice images showing a cross section of the subject;
Based on the section definition information, section specifying means for specifying the section to which the section corresponding to at least one slice image included in the image belongs;
Coordinate value calculation means for calculating a coordinate value of the slice image based on a reference coordinate system in which coordinate values are defined for each section and the specified section;
An image processing apparatus comprising: Further comprising position acquisition means for acquiring position information indicating a position in the image of the anatomical feature section in which the boundary of the anatomical structure is depicted;
2. The section according to claim 1, wherein the section specifying unit specifies the section to which the section corresponding to the slice image belongs, based on the position information of the acquired anatomical feature section. Image processing device.   The image processing apparatus according to claim 2, wherein the anatomical feature section is a section in which an anatomical feature is depicted at a boundary of the anatomical structure. Further comprising position acquisition means for acquiring position information indicating the position of the anatomical feature cross section of the subject indicating the boundary of the section;
The image processing apparatus according to claim 1, wherein the coordinate value calculation unit calculates the coordinate value based on the acquired position information.   The position acquisition means estimates the position of the anatomical feature cross section outside the imaging range of the subject included in the image, thereby indicating the position of the anatomical feature cross section outside the imaging range The image processing apparatus according to claim 4, wherein information is acquired. A positioning unit that performs positioning between the acquired image and a three-dimensional reference image including a slice image in which information on the section is associated;
The section specifying means includes the section to which the cross section corresponding to the slice image included in the image belongs based on information on the section associated with the slice image included in the aligned reference image. The image processing apparatus according to claim 1, wherein the image processing apparatus is specified. The image acquisition means acquires a plurality of the images,
7. The image processing apparatus according to claim 1, further comprising an association unit that associates each of the slice images having the substantially same coordinate value in the reference coordinate system included in the acquired plurality of images. The image processing apparatus according to item 1.   The image processing apparatus according to claim 7, further comprising display control means for causing the display means to display the associated slice images in a form that can be compared.   The image according to any one of claims 1 to 8, wherein the section specifying unit specifies the section to which the cross section corresponding to the slice image belongs when the plurality of images are acquired. Processing equipment. The information acquisition means acquires a plurality of the section definition information,
The image acquisition means acquires a plurality of the images,
The image processing apparatus further includes:
Corresponding means for associating each slice image representing substantially the same position in the plurality of acquired images for each of the plurality of section definition information;
A selection means for selecting one piece of information from information related to the plurality of section definition information in accordance with an area to which the user pays attention;
Display control means for causing the display means to display each of the associated slice images based on the information selected by the selection means;
The image processing apparatus according to claim 1, further comprising:   The image processing apparatus according to claim 10, wherein the selection unit selects the information based on a display condition of the image on the display unit.   The image processing apparatus according to claim 11, wherein the display condition includes at least a display parameter related to a density value of the image.   13. The image processing apparatus according to claim 10, wherein each of the plurality of section definition information is section definition information based on different anatomical structures. An information acquisition step for acquiring section definition information defining a plurality of sections obtained by dividing the human body along the body axis direction;
An image acquisition step of acquiring a three-dimensional image including a plurality of slice images showing a cross section of the subject;
A section specifying step for specifying the section to which the cross section corresponding to at least one slice image included in the image belongs based on the section definition information;
A coordinate value calculating step for calculating a coordinate value of the slice image based on a reference coordinate system in which a coordinate value is defined for each section and the specified section;
An image processing method comprising: The image processing device
Information acquisition means for acquiring section definition information defining a plurality of sections obtained by dividing the human body along the body axis direction;
Image acquisition means for acquiring a three-dimensional image including a plurality of slice images showing a cross section of the subject;
Based on the section definition information, section specifying means for specifying the section to which the section corresponding to at least one slice image included in the image belongs;
Coordinate value calculation means for calculating the coordinate value of the slice image based on a reference coordinate system in which coordinate values are defined for each section and the specified section.
A program characterized by functioning as

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