Image processing device, method and program

Abstract

In the present invention, a processor: derives, from each of a plurality of tomographic images expressing a tomographic plane of a subject, a linear structure image expressing a high frequency linear structure; derives a feature amount representing the feature of the linear structure from the linear structure image; selects, on the basis of the feature amount and for each corresponding pixel in the tomographic images or in high frequency tomographic images expressing high frequency components of the tomographic images, at least one tomographic image or high frequency tomographic image including the linear structure, or a predetermined tomographic image or high frequency tomographic image; and derives a composite two-dimensional image on the basis of the selected tomographic images or high frequency tomographic images.

Description

[Technical field]

[0001] The present disclosure relates to an image processing device, a method, and a program. [Background technology]

[0002] In recent years, imaging diagnosis using a radiation imaging device (called mammography) that captures the breast has been attracting attention in order to promote early detection of breast cancer. In addition, tomosynthesis imaging has been proposed in mammography, in which a radiation source is moved to irradiate the breast from multiple radiation source positions to capture images, and multiple projection images thus obtained are reconstructed to generate a tomographic image with a desired tomographic plane emphasized. In tomosynthesis imaging, depending on the characteristics of the imaging device and the required tomographic image, the radiation source is moved parallel to the radiation detector or moved in a circular or elliptical arc to capture multiple projection images by capturing images of the breast at multiple radiation source positions, and these projection images are reconstructed using a backprojection method such as a simple backprojection method or a filtered backprojection method, or an iterative reconstruction method, to generate a tomographic image.

[0003] By generating such tomographic images on multiple slices of the breast, it is possible to separate structures that overlap in the depth direction of the slices within the breast. This makes it possible to discover abnormal areas such as lesions that are difficult to detect in 2D images obtained by conventional plain radiography, in which radiation is irradiated onto the subject from a predetermined direction (hereinafter referred to as plain 2D images).

[0004] In addition, a technology is known in which a pseudo two-dimensional image equivalent to a simple two-dimensional image (hereinafter referred to as a composite two-dimensional image) is generated by synthesizing multiple tomographic images obtained by tomosynthesis imaging and having different distances (height direction positions) from the detection surface of a radiation detector toward the radiation source by using an addition method, an averaging method, a maximum value projection method, a minimum value projection method, or the like (see JP 2014-128716 A).

[0005] On the other hand, in the medical field, a computer-aided diagnosis system (CAD, hereinafter referred to as CAD) is known that automatically detects structures such as abnormal shadows in an image and highlights the detected structures. For example, CAD is used to detect diagnostically important structures such as tumors, spicules, and calcifications from tomographic images acquired by tomosynthesis imaging. In addition, a method has been proposed for generating a composite two-dimensional image from multiple tomographic images acquired by tomosynthesis imaging of a breast, in which a region of interest including a structure is detected by CAD and the detected region of interest is synthesized on a two-dimensional image acquired by, for example, a projection image or a plain imaging, thereby generating a composite two-dimensional image (see U.S. Patent No. 8,983,156). In addition, a method has been proposed for generating a composite two-dimensional image by averaging tomographic images including only structures detected by CAD (see U.S. Patent No. 9,792,703). Summary of the Invention [Problem to be solved by the invention]

[0006] However, in the composite 2D image generated by the method described in U.S. Pat. No. 8,983,156, the structure of interest synthesized in the 2D image is only the structure of interest acquired from one tomographic image. Therefore, when a linear structure including a faint thin line, such as a mammary gland structure and spicules contained in the breast, exists across multiple tomographic images, the state in which the structure exists in the depth direction in which the tomographic images are arranged cannot be reflected in the composite 2D image. In addition, the method described in U.S. Pat. No. 9,792,703 averages the structures of interest contained in multiple tomographic images. Therefore, for example, fine structures of interest such as calcifications contained in the breast and linear structures such as mammary glands and spicules are faint and difficult to see.

[0007] The present invention has been made in consideration of the above circumstances, and has an object to make it easier to see fine structures contained in a subject in a synthetic two-dimensional image. [Means for solving the problem]

[0008] The image processing device according to the present disclosure includes at least one processor, The processor A line structure image representing a high frequency line structure is derived from each of a plurality of tomographic images representing a tomographic plane of the subject; A feature quantity that represents the characteristics of the lineation is derived from the lineation image, Selecting at least one tomographic image or high-frequency tomographic image including a line structure or a predetermined tomographic image or high-frequency tomographic image for each corresponding pixel in each of the tomographic image or high-frequency tomographic image representing a high-frequency component of the tomographic image based on the feature amount; The apparatus is configured to derive a composite two-dimensional image based on the selected tomographic or high frequency tomographic images.

[0009] In the image processing device according to the present disclosure, the processor selects at least one tomographic image or high-frequency tomographic image including a line structure, The apparatus may be configured to derive a composite two-dimensional image based on a pre-composite image that is generated in advance based on the tomographic image or the high-frequency tomographic image, and on the selected tomographic image or the high-frequency tomographic image.

[0010] In the image processing device according to the present disclosure, the processor detects a predetermined structure of interest from the tomographic image or the high frequency tomographic image;

[0011] For a corresponding pixel of a tomographic image or a high-frequency tomographic image in which a structure of interest is detected, the tomographic image or high-frequency tomographic image in which the structure of interest is detected may be selected instead of a tomographic image or high-frequency tomographic image including a line structure or a predetermined tomographic image or high-frequency tomographic image.

[0012] In addition, in the image processing device according to the present disclosure, the subject may be a breast and the structure of interest may be calcification.

[0013] In addition, in the image processing device according to the present disclosure, the subject may be a breast, and the line structures may be mammary glands and spicules.

[0014] In addition, in the image processing device according to the present disclosure, the processor may be configured to derive pixel values ​​of the line structure image as feature amounts.

[0015] In addition, in the image processing device according to the present disclosure, the processor may be configured to derive a variance value for each pixel of the line structure image as a feature amount.

[0016] In addition, in the image processing device according to the present disclosure, the processor may be configured to derive the feature amount by converting pixel values ​​of the line structure image.

[0017] In the image processing device according to the present disclosure, the processor may be configured to select a tomographic image or a high-frequency tomographic image including a line structure having a feature amount with a magnitude that is one or more of the top one.

[0018] In addition, in the image processing device according to the present disclosure, the processor may be configured to select a tomographic image or a high-frequency tomographic image including a line structure whose feature amount is equal to or larger than a predetermined threshold value.

[0019] In addition, the image processing method according to the present disclosure includes deriving a line structure image representing a high-frequency line structure from each of a plurality of tomographic images representing a tomographic plane of a subject; A feature quantity that represents the characteristics of the lineation is derived from the lineation image, Selecting at least one tomographic image or high-frequency tomographic image including a line structure or a predetermined tomographic image or high-frequency tomographic image for each corresponding pixel in each of the tomographic image or high-frequency tomographic image representing a high-frequency component of the tomographic image based on the feature amount; A composite two-dimensional image is derived based on the selected tomographic image or high frequency tomographic image.

[0020] The image processing method according to the present disclosure may be provided as a program for causing a computer to execute the method. Effect of the Invention

[0021] According to the present disclosure, fine structures contained in a subject can be easily seen in a composite two-dimensional image. [Brief description of the drawings]

[0022] [Figure 1] FIG. 1 is a schematic configuration diagram of a radiation image capturing system to which an image processing device according to a first embodiment of the present disclosure is applied; [Diagram 2] A view of the radiation image capture device from the direction of arrow A in FIG. 1. [Diagram 3] FIG. 1 is a diagram showing a schematic configuration of an image processing apparatus according to a first embodiment; [Figure 4] FIG. 1 is a diagram showing a functional configuration of an image processing apparatus according to a first embodiment; [Diagram 5] FIG. 1 is a diagram for explaining acquisition of a projection image. [Figure 6] FIG. 1 is a diagram for explaining generation of a tomographic image; [Figure 7] FIG. 1 shows an example of a tomographic image. [Figure 8] FIG. 1 shows a low-frequency tomographic image and a high-frequency tomographic image. [Figure 9] A diagram showing lineation images [Figure 10] FIG. 1 is a diagram for explaining selection of high-frequency tomographic images in the first embodiment; [Figure 11] FIG. 1 is a diagram for explaining generation of a synthetic high-frequency image. [Figure 12] FIG. 1 is a diagram for explaining generation of a synthetic low-frequency image; [Figure 13] FIG. 1 is a diagram showing a display screen of a composite two-dimensional image in the first embodiment. [Figure 14] 1 is a flowchart showing the processing performed in the first embodiment. [Figure 15] FIG. 13 is a diagram showing a functional configuration of an image processing apparatus according to a second embodiment. [Figure 16]FIG. 13 is a diagram for explaining selection of a tomographic image in the second embodiment; [Figure 17] FIG. 13 is a diagram showing a display screen of a composite two-dimensional image in the second embodiment. [Figure 18] 1 is a flowchart showing the process performed in the second embodiment. [Figure 19] FIG. 13 is a diagram for explaining selection of a tomographic image in the third embodiment; [Figure 20] 11 is a flowchart showing the process performed in the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. Fig. 1 is a schematic diagram of a radiographic imaging system to which an image processing device according to an embodiment of the present disclosure is applied, and Fig. 2 is a diagram of a mammography imaging device in the radiographic imaging system as viewed from the direction of arrow A in Fig. 1. As shown in Fig. 1, a radiographic imaging system 100 according to this embodiment is for capturing images of a subject breast M from multiple radiation source positions to obtain multiple radiographic images, i.e., multiple projection images, in order to perform tomosynthesis imaging of the breast and generate a tomographic image. The radiographic imaging system 100 according to this embodiment includes a mammography imaging device 1, a console 2, an image storage system 3, and an image processing device 4.

[0024] The mammography device 1 has an arm unit 12 connected to a base (not shown) by a rotation shaft 11. An imaging table 13 is attached to one end of the arm unit 12, and a radiation irradiation unit 14 is attached to the other end so as to face the imaging table 13. The arm unit 12 is configured so that only the end to which the radiation irradiation unit 14 is attached can rotate, which makes it possible to rotate only the radiation irradiation unit 14 while fixing the imaging table 13.

[0025] A radiation detector 15 such as a flat panel detector is provided inside the imaging stand 13. The radiation detector 15 has a radiation detection surface 15A. Also provided inside the imaging stand 13 are a circuit board and the like on which are provided a charge amplifier that converts a charge signal read out from the radiation detector 15 into a voltage signal, a correlated double sampling circuit that samples the voltage signal output from the charge amplifier, and an AD (Analog-Digital) conversion unit that converts the voltage signal into a digital signal.

[0026] A radiation source 16 is housed inside the radiation irradiation unit 14. The radiation source 16 emits X-rays as radiation, and the timing at which radiation is emitted from the radiation source 16 and the radiation generation conditions in the radiation source 16, i.e., the selection of the target and filter materials, the tube voltage, the irradiation time, etc., are controlled by the console 2.

[0027] The arm 12 is also provided with a compression plate 17 that is disposed above the imaging table 13 and presses down on the breast M, a support section 18 that supports the compression plate 17, and a movement mechanism 19 that moves the support section 18 in the vertical direction in Figures 1 and 2. The distance between the compression plate 17 and the imaging table 13, i.e., the compression thickness, is input to the console 2.

[0028] The console 2 has a function of controlling the mammography apparatus 1 using imaging orders and various information acquired from a RIS (Radiology Information System) (not shown) or the like via a network such as a wireless communication LAN (Local Area Network) and using instructions given directly by a technician or the like. Specifically, the console 2 causes the mammography apparatus 1 to perform tomosynthesis imaging of the breast M, thereby acquiring a plurality of projection images as described below, and reconstructing the plurality of projection images to generate a plurality of tomographic images. As an example, in this embodiment, a server computer is used as the console 2.

[0029] The image storage system 3 is a system that stores image data such as radiation images and tomographic images captured by the mammography device 1. The image storage system 3 extracts from the stored images an image corresponding to a request from the console 2, the image processing device 4, etc., and transmits it to the device that issued the request. A specific example of the image storage system 3 is a PACS (Picture Archiving and Communication System).

[0030] Next, an image processing device according to a first embodiment will be described. First, a hardware configuration of the image processing device according to the first embodiment will be described with reference to FIG. 3. As shown in FIG. 3, the image processing device 4 is a computer such as a workstation, a server computer, or a personal computer, and includes a CPU (Central Processing Unit) 21, a non-volatile storage 23, and a memory 26 as a temporary storage area. The image processing device 4 also includes a display 24 such as a liquid crystal display, an input device 25 such as a keyboard and a mouse, and a network I / F (InterFace) 27 connected to a network (not shown). The CPU 21, the storage 23, the display 24, the input device 25, the memory 26, and the network I / F 27 are connected to a bus 28. The CPU 21 is an example of a processor in the present disclosure.

[0031] The storage 23 is realized by a hard disk drive (HDD), a solid state drive (SSD), a flash memory, etc. The storage 23 as a storage medium stores the image processing program 22 installed in the image processing device 4. The CPU 21 reads the image processing program 22 from the storage 23, expands it in the memory 26, and executes the expanded image processing program 22.

[0032] The image processing program 22 is stored in a storage device of a server computer connected to the network or in a network storage in an externally accessible state, and is downloaded and installed in a computer constituting the image processing device 4 upon request. Alternatively, the program is recorded on a recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) and distributed, and is installed in a computer constituting the image processing device 4 from the recording medium.

[0033] Next, the functional configuration of the image processing device according to the first embodiment will be described. FIG. 4 is a diagram showing the functional configuration of the image processing device according to the first embodiment. As shown in FIG. 4, the image processing device 4 includes an image acquisition unit 30, a line structure image derivation unit 31, a feature amount derivation unit 32, a structure of interest detection unit 33, a selection unit 34, a synthesis unit 35, and a display control unit 36. Then, the CPU 21 executes the image processing program 22, so that the image processing device 4 functions as the image acquisition unit 30, the line structure image derivation unit 31, the feature amount derivation unit 32, the structure of interest detection unit 33, the selection unit 34, the synthesis unit 35, and the display control unit 36.

[0034] The image acquisition unit 30 acquires a tomographic image from the console 2 or the image storage system 3 via the network I / F 27 .

[0035] Here, a description will be given of tomosynthesis imaging and generation of tomographic images in the console 2. When performing tomosynthesis imaging to generate tomographic images, the console 2 moves the radiation source 16 by rotating the arm unit 12 around the rotation axis 11, irradiates the breast M, which is the subject, with radiation under predetermined imaging conditions for tomosynthesis imaging at multiple radiation source positions determined by the movement of the radiation source 16, detects the radiation that has passed through the breast M with the radiation detector 15, and obtains multiple projection images Gi (i=1 to n, n is the number of radiation source positions, for example, n=15) at the multiple radiation source positions.

[0036] Fig. 5 is a diagram for explaining acquisition of projection images Gi. As shown in Fig. 5, radiation source 16 is moved to each radiation source position S1, S2, ..., Sn, and radiation source 16 is driven at each radiation source position to irradiate breast M with radiation. The radiation that has passed through breast M is detected by radiation detector 15, whereby projection images G1, G2, ..., Gn are acquired corresponding to each radiation source position S1 to Sn. Note that the same dose of radiation is irradiated to breast M at each radiation source position S1 to Sn.

[0037] 5, radiation source position Sc is the radiation source position where the optical axis X0 of the radiation emitted from the radiation source 16 is perpendicular to the detection surface 15A of the radiation detector 15. The radiation source position Sc is referred to as the reference radiation source position Sc.

[0038] Then, the console 2 reconstructs the multiple projection images Gi to generate a tomographic image in which a desired tomographic plane of the breast M is emphasized. Specifically, the console 2 reconstructs the multiple projection images Gi using a well-known back projection method such as a simple back projection method or a filtered back projection method, and generates multiple tomographic images Dj (j=1 to m) in each of the multiple tomographic planes of the breast M as shown in FIG. 6. At this time, a three-dimensional coordinate position in a three-dimensional space including the breast M is set, and pixel values ​​of corresponding pixels in the multiple projection images Gi are reconstructed for the set three-dimensional coordinate position, and the pixel value of the coordinate position is calculated. In the first embodiment, the pixel value of the tomographic image Dj is larger as the luminance is higher (i.e., white) and smaller as the luminance is lower (i.e., black). In addition, the pixel value is set to be equal to or greater than 0.

[0039] The console 2 transfers the generated tomographic images Dj directly to the image processing device 4 or to the image storage system 3 .

[0040] The line structure image derivation unit 31 derives a line structure image representing a high-frequency line structure from each of the multiple tomographic images Dj. The high-frequency line structure in this embodiment is a linear structure having a thickness that cannot be clearly expressed in a composite two-dimensional image by using the methods described in the above-mentioned U.S. Pat. No. 8,983,156 and U.S. Pat. No. 9,792,703. Specifically, the high-frequency line structure is a structure having a thickness of about 200 to 300 μm or less in a structure contained in the breast M. Examples of such high-frequency line structures include mammary glands and spicules contained in the breast M.

[0041] On the other hand, examples of lesions contained in the breast M include masses and the like. Masses have structures that are larger than calcifications and spicules. In this embodiment, structures such as masses that are larger than calcifications and spicules are referred to as low-frequency structures.

[0042] FIG. 7 is a diagram showing an example of a tomographic image. Six tomographic images D1 to D6 are shown in FIG. 7. As shown in FIG. 7, the tomographic image D1 includes a calcification K11. The tomographic image D2 includes line structures K21, K22 and a low-frequency structure K23 such as a mass. The tomographic image D3 includes line structures K31, K32 and a low-frequency structure K33. The tomographic image D4 includes line structures K41, K42 and a low-frequency structure K43. The tomographic image D5 includes line structures K51, K52 and a low-frequency structure K53. The tomographic image D6 includes a calcification K61. FIG. 7 is a schematic diagram showing various structures included in the breast M in the tomographic image Dj, and differs from the way in which the structures are actually included.

[0043] In order to derive a line structure image, the line structure image derivation unit 31 first derives high frequency components of each of the multiple tomographic images Dj. Specifically, each tomographic image Dj is reduced to derive a low frequency tomographic image DLj representing the low frequency component of the tomographic image Dj. Then, the low frequency tomographic image DLj is enlarged to the same size as the original tomographic image Dj, and the enlarged low frequency tomographic image DLj is subtracted from the original tomographic image Dj to derive a high frequency tomographic image DHj representing the high frequency component of the tomographic image Dj. Note that instead of reducing the tomographic image Dj, the low frequency tomographic image DLj may be derived by filtering the tomographic image Dj using a low pass filter. Also, the high frequency tomographic image DHj representing the high frequency component of the tomographic image Dj may be derived by filtering the tomographic image Dj using a high pass filter. Moreover, a high-frequency tomographic image DHj representing the high-frequency components of the tomographic image Dj may be derived by performing filtering processing using a band-pass filter that extracts high-frequency components having a thickness of about 300 μm or less in the tomographic image Dj.

[0044] Fig. 8 shows low-frequency tomographic images and high-frequency tomographic images. Fig. 8 also shows low-frequency tomographic images DL1-DL6 and high-frequency tomographic images DH1-DH6 derived from the tomographic images D1-D6 shown in Fig. 7. As shown in Fig. 8, the low-frequency tomographic images DL1-DL6 include only relatively large low-frequency structures such as tumors included in the tomographic images D1-D6. The high-frequency tomographic images DH1-DH6 include only relatively small high-frequency structures such as spicules, mammary glands, and calcifications.

[0045] In the low-frequency tomographic images DLj, low-frequency structures are represented by high brightness (i.e., large pixel values), whereas in the high-frequency tomographic images DHj, high-frequency structures are represented by high brightness.

[0046] Next, the line structure image derivation unit 31 extracts high frequency line structures by applying a directional filter according to the direction of pixel connection of the high frequency tomographic image DHj, and derives a high frequency line structure image DSj. Here, the directional filter is a two-dimensional filter that has a large weight in each of the up-down direction, the left-right direction, and the two diagonal directions within the filter, and smoothes the image in other parts. A directional filter is prepared for each of the up-down direction, the left-right direction, and the two diagonal directions.

[0047] Fig. 9 is a diagram showing lineation images. Fig. 9 shows lineation images DS1 to DS6 derived from the high-frequency tomographic images DH1 to DH6 shown in Fig. 8. As shown in Fig. 9, the lineation images DS1 to DS6 include only linear structures such as mammary glands and spicules included in the tomographic image Dj. In the lineation images DSj, linear structures such as mammary glands and spicules included in the tomographic image Dj are represented by high brightness (i.e., large pixel values).

[0048] The line structure image derivation unit 31 may derive the line structure image DSj by performing filtering processing on the tomographic image Dj using a Sobel filter, a Laplacian filter, or the like. Alternatively, the line structure image DSj may be derived by extracting line structures from the tomographic image Dj using CAD.

[0049] The feature amount deriving unit 32 derives a feature amount representing the feature of the line structure from the line structure image DSj. In the first embodiment, the variance value of the pixel value in each pixel of the line structure image DSj is derived as the feature amount. Specifically, the feature amount deriving unit 32 sets a region of interest of a predetermined size for each pixel of the line structure image DSj. The size of the region of interest can be, for example, 5×5 pixels, but is not limited thereto, and can be any size, such as 3×3 pixels or 7×7 pixels. In addition, the shape of the region of interest is not limited to a rectangle, and can be any shape, such as a circle.

[0050] The feature amount deriving unit 32 calculates the variance value σ of each pixel of the line structure image DSj by using the pixel values ​​of the pixels in the region of interest according to the following formula (1): 2 is derived as a feature quantity that represents the characteristics of the line structure. In formula (1), r(xi, yi) is the pixel value of each pixel in the line structure image DSj, rm is the average pixel value in the region of interest, and Σ is the (r(xi, yi)-rm) 2 Shows the sum of. σ 2 (x,y)=Σ(r(xi,yi)-rm) 2 (1)

[0051] The structure of interest detection unit 33 detects calcification from each of the tomographic images Dj or the high-frequency tomographic images DHj. Calcification is an example of a structure of interest according to the present disclosure. In order to detect calcification, the structure of interest detection unit 33 sets a region of interest of a predetermined size for each pixel. The size of the region of interest can be, for example, 5×5 pixels, but is not limited thereto, and can be any size, such as 3×3 pixels or 7×7 pixels. In addition, the shape of the region of interest is not limited to a rectangle, and can be any shape, such as a circle. In the following description, detection of calcification from the tomographic images Dj will be described, but calcification can also be detected from the high-frequency tomographic images DHj in the same way as in the tomographic images Dj.

[0052] The structure of interest detection unit 33 calculates the variance value σ1 of each pixel of the tomographic image Dj by using the pixel values ​​of the pixels in the region of interest according to the following formula (2): 2 In formula (2), r1(x1i, y1i) is the pixel value of each pixel of the tomographic image Dj, r1m is the average pixel value in the region of interest, and Σ is the (r1(x1i, y1i)-r1m) in the region of interest. 2 Shows the sum of. σ1 2 (x,y)=Σ(r1(x1i,y1i)-r1m) 2 (2)

[0053] The structure of interest detection unit 33 detects the variance value σ 2A pixel having a luminance equal to or greater than a predetermined threshold value Th1 is detected as a pixel of calcification. The detection of calcification is not limited to the method using the variance value. Pixels of calcification may be detected by a filtering process using a filter capable of extracting pixels having a luminance equal to or greater than a predetermined threshold value Th2. Pixels of calcification may also be detected from the tomographic image Dj by CAD.

[0054] The selection unit 34 selects high-frequency tomographic images DHj to be used for generating a composite two-dimensional image, which will be described later, for each corresponding pixel in each of the high-frequency tomographic images DHj based on the feature amount derived by the feature amount derivation unit 32 and the calcification detected by the structure of interest detection unit 33. In the first embodiment, at least one high-frequency tomographic image including a line structure or a predetermined high-frequency tomographic image is selected for each corresponding pixel in each of the high-frequency tomographic images DHj. In particular, in the first embodiment, among the high-frequency tomographic images DHj, a maximum of three high-frequency tomographic images having a large feature amount in the line structure image DSj are selected as high-frequency tomographic images including a line structure for each corresponding pixel between the high-frequency tomographic images DHj. Specifically, the selection unit 34 compares the feature amount of the pixel corresponding to the pixel of interest in the line structure image DSj with the corresponding pixel of interest in the high-frequency tomographic image DHj. Then, the selection unit 34 specifies a maximum of three line structure images DSj having a large feature amount.

[0055] For example, if the feature values ​​of corresponding pixels of interest in six line structure images DS1 to DS6 are 10, 30, 10, 40, 20, and 50, respectively, the selection unit 34 specifies line structure images DS2, DS4, and DS6 as line structure images with large feature values ​​for the pixel of interest. The selection unit 34 then selects high frequency tomographic images DH2, DH4, and DH6 corresponding to the specified line structure images DS2, DS4, and DS6 as high frequency tomographic images DHj containing line structures for the pixel of interest. Note that up to three high frequency tomographic images DHj with feature values ​​equal to or greater than a predetermined threshold value Th3 may be selected as high frequency tomographic images containing line structures.

[0056] The number of selected high-frequency tomographic images DHj including line structures is not limited to a maximum of three. For example, for each corresponding pixel between the high-frequency tomographic images DHj, only one high-frequency tomographic image having the maximum feature value in the line structure image DSj may be selected as the high-frequency tomographic image including line structures. Also, all high-frequency tomographic images having feature values ​​equal to or greater than a predetermined threshold value Th4 in the line structure image DSj may be selected as the high-frequency tomographic image including line structures. Also, when the number of high-frequency tomographic images having feature values ​​greater than 0 among the six high-frequency tomographic images DH1 to DH6 is less than three, the selection unit 34 may select one or two high-frequency tomographic images having feature values ​​greater than 0 as the high-frequency tomographic images including line structures.

[0057] Furthermore, when all the feature values ​​in the line structure image DSj are 0 for corresponding pixels among the high frequency tomographic images DHj, the selection unit 34 selects all the high frequency tomographic images DHj for that pixel as the predetermined high frequency tomographic images. Note that, among the corresponding pixels among the high frequency tomographic images DHj, the high frequency tomographic image DHj having the maximum pixel value (i.e., the maximum brightness) may be selected as the predetermined high frequency tomographic image. Furthermore, among the corresponding pixels among the high frequency tomographic images DHj, a predetermined number (for example, up to three) of high frequency tomographic images DHj in descending order of pixel value may be selected as the predetermined high frequency tomographic images.

[0058] On the other hand, in the first embodiment, when calcification is detected in a corresponding pixel between the high frequency tomographic images DHj, the selection unit 34 selects the high frequency tomographic image in which the calcification is detected, regardless of the magnitude of the feature amount of the line structure derived by the feature amount derivation unit 32. For example, assume that calcification is detected in a pixel of interest between the high frequency tomographic images DHj. In that case, even if a high frequency tomographic image including a line structure is selected for the pixel of interest, the selection unit 34 selects the high frequency tomographic image in which the calcification is detected instead of the high frequency tomographic image including the line structure. In addition, when a predetermined high frequency tomographic image is selected for the pixel of interest, if calcification is detected in at least one of the high frequency tomographic images, the selection unit 34 selects the high frequency tomographic image in which the calcification is detected instead of the predetermined high frequency tomographic image.

[0059] If the number of high frequency tomographic images in which calcification is detected at the pixel of interest is three or more, the selection unit 34 may select up to three high frequency tomographic images corresponding to the tomographic images in which calcification is detected, instead of the three high frequency tomographic images including the line structure. In this case, the selection unit 34 selects the top three high frequency tomographic images with the largest variance values ​​when calcification is detected.

[0060] Furthermore, when the number of high frequency tomographic images in which calcification is detected at the pixel of interest is two or less, the selection unit 34 may select two or one high frequency tomographic images in which calcification is detected, instead of two or one of the high frequency tomographic images including line structures. In this case, the one or two high frequency tomographic images including line structures may be left as they are.

[0061] For example, assume that three high frequency tomographic images DH1, DH2, and DH3 including line structures have been selected for the pixel of interest. The magnitude of the feature amount of the line structures is DH1>DH2>DH3. Also assume that calcification has been detected in the high frequency tomographic image DH4 for the pixel of interest. In this case, the selection unit 34 selects the high frequency tomographic image DH4 instead of the high frequency tomographic image DH1 having the smallest feature amount among the high frequency tomographic images DH1, DH2, and DH3 including line structures for the pixel of interest. Also assume that calcification has been detected in the high frequency tomographic images DH4, DH5, and DH6 for the pixel of interest. In this case, the selection unit 34 selects the high frequency tomographic images DH4, DH5, and DH6 instead of the high frequency tomographic images DH1, DH2, and DH3 including line structures for the pixel of interest.

[0062] FIG. 10 is a diagram for explaining the selection of a high-frequency tomographic image based on the feature amount and calcification. In FIG. 10, the selection of a high-frequency tomographic image from the six high-frequency tomographic images DH1 to DH6 shown in FIG. 8 will be explained. In FIG. 10, the high-frequency tomographic images DH1 to DH6 are shown one-dimensionally. The high-frequency tomographic images DH1 to DH6 each have 15 pixels, P1 to P15. In FIG. 10, only the pixels P1, P5, P10, and P15 of the high-frequency tomographic image DH6 are given reference symbols. In FIG. 10, the feature amount and the variance value of calcification derived from the line structure images DS1 to DS6 are shown for each pixel of the high-frequency tomographic images DH1 to DH6.

[0063] In FIG. 10, the feature amount of the line structure is represented by a thick line, and the variance value of the calcification is represented by a thick white line. The feature amount of the line structure increases as the distance from the schematic high frequency tomographic images DH1 to DH6 increases upward, and the variance value when the calcification is detected increases. In addition, in this embodiment, it is assumed that the high frequency tomographic image DHj including up to three line structures whose feature amount is equal to or greater than the threshold value is selected. Therefore, only the feature amount of the line structure equal to or greater than the threshold value is shown in FIG. 10. In the following description, the same illustration method is used for the same figures as FIG. 10.

[0064] At pixel P1, the feature amount of the line structure is derived in high frequency tomographic images DH3 and DH4. In this case, at pixel P1, the selection unit 34 selects two high frequency tomographic images DH3 and DH4 that include the line structure.

[0065] At pixel P2, the feature amount of the line structure is derived from high frequency tomographic images DH1 to DH5, and the high frequency tomographic images having the top three feature amounts are high frequency tomographic images DH2 to DH4. Therefore, at pixel P2, the selection unit 34 selects the three high frequency tomographic images DH2 to DH4 that include the line structure.

[0066] At pixel P3, the feature amount of the line structure is derived from high frequency tomographic images DH1 to DH5, and the high frequency tomographic images having the top three feature amounts are the high frequency tomographic images DH1 to DH3. Therefore, at pixel P3, the selection unit 34 selects the three high frequency tomographic images DH1 to DH3 that include the line structure.

[0067] In pixel P4, the feature amount of the line structure is derived in high frequency tomographic images DH1 to DH5, and the high frequency tomographic images having the top three feature amounts are high frequency tomographic images DH2 to DH4. Meanwhile, in pixel P4, calcification is detected in two high frequency tomographic images DH3 and DH4. Therefore, in pixel P4, the selection unit 34 first selects two high frequency tomographic images DH3 and DH4 in which calcification is detected. In addition, among the high frequency tomographic images DH2 to DH4 having the top three feature amounts, the selection unit 34 selects one high frequency tomographic image DH2 including a line structure, excluding the high frequency tomographic images DH3 and DH4 already selected. Note that in pixel P4, the selection unit 34 may select only the high frequency tomographic images DH3 and DH4 in which calcification is detected.

[0068] For the pixel P5, the feature amounts of the line structures are derived in the three high frequency tomographic images DH2 to DH4. Therefore, for the pixel P5, the selection unit 34 selects the three high frequency tomographic images DH2 to DH4 that include the line structures.

[0069] For pixel P6, no feature value of the line structure is derived in any of the high frequency tomographic images DH1 to DH6. On the other hand, for pixel P6, calcification is detected in the high frequency tomographic image DH2. For this reason, the selection unit 34 selects pixel P 6 In step 2, only the high frequency tomographic image DH2 in which calcification is detected is selected.

[0070] At pixel P7, no line structure feature is derived in any of the high frequency tomographic images DH1 to DH6. Also, no calcification is detected. Therefore, at pixel P7, the selection unit 34 selects all of the high frequency tomographic images DH1 to DH6 as the predetermined high frequency tomographic images.

[0071] For the pixel P8, the feature amounts of the line structures are derived in the three high frequency tomographic images DH1 to DH3. Therefore, for the pixel P8, the selection unit 34 selects the three high frequency tomographic images DH1 to DH3 that include the line structures.

[0072] At pixel P9, line structure feature values ​​are derived from high frequency tomographic images DH1 to DH4, and the high frequency tomographic images with the top three feature values ​​are the high frequency tomographic images DH1 to DH3. Meanwhile, at pixel P9, calcification is detected in the three high frequency tomographic images DH4 to DH6. Therefore, at pixel P9, the selection unit 34 selects the three high frequency tomographic images DH4 to DH6 in which calcification is detected.

[0073] At pixel P10, the feature amount of the line structure is derived from high frequency tomographic images DH1 to DH4, and the high frequency tomographic images having the top three feature amounts are the high frequency tomographic images DH1 to DH3. Therefore, at pixel P10, the selection unit 34 selects the three high frequency tomographic images DH1 to DH3 that include the line structure.

[0074] In the pixels P11 to P13, no feature value of a line structure is derived in any of the high frequency tomographic images DH1 to DH6. Also, no calcification is detected. Therefore, in the pixels P11 to P13, the selection unit 34 selects all of the high frequency tomographic images DH1 to DH6 as the predetermined high frequency tomographic images.

[0075] At pixel P14, the feature amount of the line structure is derived in high frequency tomographic images DH2 and DH4. On the other hand, at pixel P14, calcification is detected in three high frequency tomographic images DH3 to DH5. Therefore, at pixel P14, the selection unit 34 selects the three high frequency tomographic images DH3 to DH5 in which calcification is detected.

[0076] At pixel P15, no line structure feature is derived in any of the high frequency tomographic images DH1 to DH6. Also, no calcification is detected. Therefore, at pixel P15, the selection unit 34 selects all of the high frequency tomographic images DH1 to DH6 as the predetermined high frequency tomographic images.

[0077] The synthesis unit 35 derives a synthetic two-dimensional image based on the high-frequency tomographic image selected by the selection unit 34. That is, in the region of line structures and the region of calcification, the synthesis unit 35 derives a synthetic two-dimensional image based on the high-frequency tomographic image including line structures and the high-frequency tomographic image in which calcification is detected. In the region other than the line structures and the calcification, the synthesis unit 35 derives a synthetic two-dimensional image based on a predetermined high-frequency tomographic image.

[0078] Here, in the first embodiment, the synthesis unit 35 derives a synthetic high frequency image GH1, which is a synthetic two-dimensional image for the high frequency tomographic image DHj, based on the selected high frequency tomographic image. The synthesis unit 35 also derives a synthetic low frequency image GL1, which is a synthetic two-dimensional image for the low frequency tomographic image DLj, which represents the low frequency components of the tomographic image Dj used when the line structure image derivation unit 31 derives the line structure image DSj. Then, the synthesis unit 35 performs the synthesis high frequency A composite two-dimensional image CG1 is derived from the image GH1 and the composite low-frequency image GL1.

[0079] First, derivation of the composite high frequency image GH1 will be described. In the first embodiment, it is assumed that a high frequency tomographic image is selected as described above with reference to Fig. 10. Therefore, hereinafter, composition for each of the pixels P1 to P15 will be described.

[0080] For pixel P1, two high frequency tomographic images DH3 and DH4 containing line structures are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of pixel P1 in the high frequency tomographic images DH3 and DH4 according to the feature amount (i.e., variance value), and sets the weighted average value as the pixel value of pixel P1 in the synthesized high frequency image GH1. Note that the weighting coefficient for the weighted average is derived so that the larger the feature amount, the larger the value becomes. Also, instead of the weighted average value, an additive average value may be used. The same applies to the following explanation.

[0081] For the pixel P2, three high frequency tomographic images DH2 to DH4 including a line structure are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of the pixel P2 in the high frequency tomographic images DH2 to DH4 according to the feature amount, and sets the weighted average value as the pixel value of the pixel P2 in the synthesized high frequency image GH1.

[0082] For the pixels P3, P8, and P10, three high frequency tomographic images DH1 to DH3 including line structures are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of the pixels P3, P8, and P10 in the high frequency tomographic images DH1 to DH3 according to the feature amount, and sets the weighted average value as the pixel value of the pixels P3, P8, and P10 in the synthesized high frequency image GH1.

[0083] For pixel P4, three high frequency tomographic images DH2 to DH4 are selected. Of these, two high frequency tomographic images DH3 and DH4 are selected based on the detection result of calcification. For this reason, for pixel P4, the synthesis unit 35 first derives a weighted average value of the pixel values ​​of pixel P4 in the high frequency tomographic images DH3 and DH4 according to the magnitude of the variance value at the time of calcification detection. Then, the synthesis unit 35 derives an arithmetic average value of the weighted average value of the pixel values ​​of pixel P4 in the high frequency tomographic images DH3 and DH4 and the pixel value of pixel P4 in the high frequency tomographic image DH2 including the line structure, and sets the derived arithmetic average value as the pixel value of pixel P4 in the synthesized high frequency image GH1.

[0084] For pixel P5, three high frequency tomographic images DH2 to DH4 including line structures are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of pixel P5 in the high frequency tomographic images DH2 to DH4 according to the feature amount, and sets the weighted average value as the pixel value of pixel P5 in the synthesized high frequency image GH1.

[0085] For the pixel P6, only the high frequency tomographic image DH2 in which calcification is detected is selected. Therefore, the synthesis unit 35 calculates the pixel value of the pixel P6 in the high frequency tomographic image DH2 by subtracting the pixel value of the pixel P6 from the pixel value of the pixel P6 in the synthesized high frequency image GH1. P6 The pixel value is set to be .

[0086] For the pixels P7, P11 to P13, and P15, all of the high frequency tomographic images DH1 to DH6 are selected. Therefore, the synthesis unit 35 derives the arithmetic average of the pixel values ​​of the pixels P7, P11 to P13, and P15 in the high frequency tomographic images DH1 to DH6, and sets the arithmetic average as the pixel value of the pixels P7, P11 to P13, and P15 in the synthesized high frequency image GH1.

[0087] The three high frequency tomographic images DH4 to DH6 in which calcification was detected for pixel P9 are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of pixel P9 in the high frequency tomographic images DH4 to DH6 according to the magnitude of the variance value calculated when calcification was detected, and sets the weighted average value as the pixel value of pixel P9 in the synthesized high frequency image GH1.

[0088] The three high frequency tomographic images DH3 to DH5 in which calcification was detected for pixel P14 are selected. Therefore, the synthesis unit 35 derives a weighted average of the pixel values ​​of pixel P14 in the high frequency tomographic images DH3 to DH5 according to the magnitude of the variance value calculated when calcification was detected, and sets the weighted average as the pixel value of pixel P14 in the synthesized high frequency image GH1.

[0089] Fig. 11 is a diagram for explaining generation of a composite high-frequency image. Note that Fig. 11 also uses six high-frequency tomographic images DH1 to DH6 for explanation. As shown in Fig. 11, the high-frequency tomographic images DH1 to DH6 contain line structures and calcifications, and the composite high-frequency image GH1 contains a composite of the line structures and calcifications contained in the six high-frequency tomographic images DH1 to DH6.

[0090] Here, calcification K11 contained in the tomographic image D1 is detected in the high frequency tomographic image DH1, and the calcification K11 overlaps with the line structure K21 contained in the high frequency tomographic image DH2. In this case, in the pixel corresponding to the calcification K11, the selection unit 34 selects the high frequency tomographic image DH1 in which the calcification K11 is detected, rather than the high frequency tomographic image DH2 containing the line structure. Therefore, in the composite high frequency image GH1, the calcification K11 contained in the high frequency tomographic image DH1 is overwritten on the line structure K21 contained in the high frequency tomographic image DH2.

[0091] Furthermore, calcification K61 contained in the tomographic image D6 is detected in the high frequency tomographic image DH6, and the calcification K61 overlaps with the line structure K51 contained in the high frequency tomographic image DH5. In this case, in the pixel corresponding to the calcification K61, the selection unit 34 selects the high frequency tomographic image DH6 in which the calcification K61 was detected, rather than the high frequency tomographic image DH5 containing the line structure. Therefore, in the composite high frequency image GH1, the calcification K61 contained in the high frequency tomographic image DH6 is overwritten on the line structure K51 contained in the high frequency tomographic image DH5.

[0092] On the other hand, the synthesis unit 35 derives an arithmetic average of pixel values ​​of corresponding pixels for all pixels of the low-frequency tomographic image DLj, and sets the arithmetic average as the pixel value of the synthetic low-frequency image GL1. Note that the synthesis unit 35 may derive the synthetic low-frequency image GL1 by any method, such as calculating a variance value of each pixel for each of the low-frequency tomographic images DLj and deriving a weighted average value according to the magnitude of the variance value.

[0093] FIG. 12 is a diagram for explaining generation of a composite low-frequency image. Note that FIG. 12 will be explained using six low-frequency tomographic images DL1 to DL6 shown in FIG. 8. As shown in FIG. 12, the low-frequency tomographic images DL2 to DL5 each contain low-frequency structures K23, K33, K43, and K53 such as a mass contained in the breast M. Since the structures K23, K33, and K43 contained in the low-frequency tomographic images DL2 to DL4 overlap, the composite low-frequency image GL1 contains a structure K71 in which the structures K23, K33, and K43 overlap. Since the structure K53 contained in the low-frequency tomographic image DL5 does not overlap with any of the structures contained in the other low-frequency tomographic images DL1 to DL4 and DL6, the structure K53 is included as it is in the composite low-frequency image GL1.

[0094] The synthesis unit 35 derives a synthetic two-dimensional image CG1 by synthesizing the synthetic high-frequency image GH1 and the synthetic low-frequency image GL1. As a synthesis method, a method corresponding to the derivation of the high-frequency line structure performed by the line structure image derivation unit 31 may be used. For example, when a high-frequency tomographic image is derived by subtracting an enlarged low-frequency tomographic image DLj from a tomographic image Dj, the synthetic two-dimensional image CG1 is derived using, for example, a method described in JP 2018-029746 A. Specifically, the low-frequency tomographic image DLj is enlarged by an interpolation calculation so as to have the same size as the original tomographic image Dj, and the enlarged low-frequency tomographic image DLj and the synthetic high-frequency image GH1 are added to derive the synthetic two-dimensional image CG1. The addition may be a weighted addition. In this case, it is preferable to make the weighting coefficient for the synthetic high-frequency image GH1 larger than that for the synthetic low-frequency image GL1.

[0095] The display control unit 36 ​​displays the composite two-dimensional image CG1 derived by the synthesis unit 35 on the display 24. FIG. 13 is a diagram showing a display screen of the composite two-dimensional image in the first embodiment. As shown in FIG. 13, the composite two-dimensional image CG1 is displayed on the display screen 50 of the display 24. The composite two-dimensional image CG1 shown in FIG. 13 is derived from the composite high-frequency image GH1 shown in FIG. 11 and the composite low-frequency image GL1 shown in FIG. 12. In FIG. 13, not all reference numbers are given to the structures included in the tomographic image shown in FIG. 7. The composite two-dimensional image CG1 shown in FIG. 13 includes line structures, calcifications, and low-frequency structures included in the tomographic image Dj. In particular, the calcification K11 included in the tomographic image D1 and the line structures K21 included in the tomographic image D2 overlap, but the calcifications are easily visible by replacing the line structures K21 with the calcifications K11. Furthermore, calcification K61 included in cross-sectional image D6 overlaps with lineation K51 included in cross-sectional image D5, but the lineation K51 is replaced by calcification K61, making the calcification easier to see.

[0096] Next, the process performed in the first embodiment will be described. FIG. 14 is a flow chart showing the process performed in the first embodiment. It is assumed that the plurality of tomographic images Dj are acquired in advance and stored in the storage 23. When the input device 25 receives an instruction from the operator to start the process, the process starts, and the lineation image derivation unit 31 derives a lineation image DSj from the plurality of tomographic images Dj (step ST1). Next, the feature derivation unit 32 derives a feature representing the feature of the lineation from each of the plurality of lineation images DSj (step ST2). In addition, the structure of interest detection unit 33 detects calcification as a structure of interest from each of the plurality of tomographic images Dj or high frequency tomographic images DHj (step ST3).

[0097] Next, the selection unit 34 selects a high frequency tomographic image for each corresponding pixel in each of the high frequency tomographic images DHj representing the high frequency components of the tomographic image Dj (step ST4). That is, the selection unit 34 selects at least one high frequency tomographic image including a line structure or a predetermined high frequency tomographic image for each corresponding pixel in each of the high frequency tomographic images DHj based on the feature amount. Also, for a pixel corresponding to a high frequency tomographic image in which calcification has been detected, the high frequency tomographic image in which calcification has been detected is selected instead of the high frequency tomographic image including a line structure or the predetermined high frequency tomographic image.

[0098] Furthermore, the synthesis unit 35 derives a synthetic two-dimensional image CG1 based on the selected high frequency tomographic images (step ST5), and the display control unit 36 ​​displays the synthetic two-dimensional image CG1 on the display 24 (step ST6), and the process ends.

[0099] In this way, in the first embodiment, the line structure image DSj is derived from the tomographic image Dj, a feature amount representing the feature of the line structure is derived from the line structure image DSj, and at least one high-frequency tomographic image including the line structure or a predetermined high-frequency tomographic image is selected for each corresponding pixel in each high-frequency tomographic image DHj based on the feature amount. Then, the composite two-dimensional image CG1 is derived based on the selected high-frequency tomographic image. Here, when the line structures included in the breast M overlap in the depth direction of the breast M (i.e., the direction in which the tomographic images Dj are arranged), the feature amount becomes large, and therefore the high-frequency tomographic image including the line structure is selected. Therefore, in the composite two-dimensional image CG1, the line structures are clearly included without being blurred. Therefore, according to this embodiment, it is possible to make the fine structures included in the breast M easier to see in the composite two-dimensional image CG1.

[0100] In the first embodiment, calcification is detected as a structure of interest from the tomographic image Dj or the high-frequency tomographic image DHj, and in a pixel where calcification is detected, the high-frequency tomographic image where the calcification is detected is selected instead of the high-frequency tomographic image DHj including a line structure or a predetermined high-frequency tomographic image DHj. Here, calcification is an important structure in diagnosing breast cancer. Therefore, even if calcification included in the breast M overlaps with a line structure in the depth direction of the breast M, the high-frequency tomographic image including the calcification is selected, so that the calcification is clearly included in the composite two-dimensional image CG1 without being hidden by other structures in the breast M. Therefore, the calcification included in the breast M can be easily seen in the composite two-dimensional image CG1.

[0101] In the first embodiment, when all the feature amounts are 0 for each corresponding pixel between the high frequency tomographic images DHj, a predetermined high frequency tomographic image is selected, but this is not limited to this. When all the feature amounts are less than a predetermined threshold value Th5 for each corresponding pixel between the high frequency tomographic images DHj, a predetermined high frequency tomographic image may be selected. In this case, when there is a pixel whose feature amount is equal to or greater than the threshold value Th5, the high frequency tomographic image DHj including that pixel is selected as the high frequency tomographic image including the line structure.

[0102] Next, a second embodiment of the present disclosure will be described. Fig. 15 is a diagram showing a functional configuration of an image processing device according to the second embodiment. In Fig. 15, the same configuration as in Fig. 4 is given the same reference numerals, and detailed description will be omitted. Compared with the image processing device 4 according to the first embodiment shown in Fig. 4, the image processing device 4A according to the second embodiment differs from the first embodiment in that it does not include a structure of interest detection unit 33. In the second embodiment, the processes performed by the feature derivation unit 32, the selection unit 34, and the synthesis unit 35 differ from those of the first embodiment.

[0103] In the second embodiment, the feature deriving unit 32 derives the feature by converting the pixel value of each pixel of the linear structure image DSj. In the second embodiment, the pixel value of the linear structure image DSj is assumed to have a higher value as the luminance (i.e., white) increases. In the second embodiment, the average pixel value of the entire linear structure image is assumed to be 0. That is, a pixel with high luminance (i.e., white) has a positive value, and a pixel with low luminance (i.e., black) has a negative value. In the second embodiment, the pixel value of each pixel of the linear structure image DSj is converted, and the absolute value of the converted value is used as the feature. Specifically, the absolute value of the value obtained by adding a constant value to the pixel value of each pixel of the linear structure image DSj is used as the feature. In addition, when the pixel value of each pixel of the linear structure image DSj is 0 or more, the absolute value of the pixel value of each pixel of the linear structure image DSj multiplied by a1 (a1>1) or the value obtained by adding a constant value may be used as the feature. In addition, when the pixel value of each pixel of the linear structure image DSj is 0 or less, the absolute value of the pixel value of each pixel of the linear structure image DSj multiplied by a2 (a2<1) or added with a constant value may be used as the feature value. As a result, the feature value of the linear structure image DSj increases as the linear structure resemblance increases.

[0104] In addition, for each pixel of the linear structure image DSj, if the pixel value is smaller as the brightness is higher, the feature amount may be derived by multiplying the pixel value of each pixel of the linear structure image DSj by a3 (a3>1) or subtracting a fixed value when the pixel value of each pixel of the linear structure image DSj is equal to or less than 0. In addition, if the average pixel value of the entire image is not 0, the pixel value may be converted so that the average pixel value becomes 0, and then the feature amount may be calculated.

[0105] Here, in the first embodiment, the selection unit 34 selects the high-frequency tomographic images DHj, but in the second embodiment, the selection unit 34 selects, instead of the high-frequency tomographic images, a tomographic image Dj to be used for generating a composite two-dimensional image based on the feature amount for each corresponding pixel of the tomographic image Dj. In the second embodiment, at least one tomographic image including a line structure or a predetermined tomographic image is selected for each corresponding pixel in each of the tomographic images Dj.

[0106] Since the image processing device 4A according to the second embodiment does not include the structure of interest detection unit 33, the selection unit 34 in the second embodiment selects a tomographic image without considering calcification. That is, among the tomographic images Dj, up to three tomographic images having large feature amounts in the line structure image DSj are selected as tomographic images including line structures for each corresponding pixel between the tomographic images Dj. Specifically, the selection unit 34 compares the feature amounts of pixels corresponding to the pixel of interest in the line structure image DSj with the corresponding pixel of interest in the tomographic image Dj. Then, the selection unit 34 identifies up to three line structure images DSj having large feature amounts.

[0107] For example, if the feature values ​​of corresponding pixels of interest in six line structure images DS1 to DS6 are 10, 30, 10, 40, 20, and 50, respectively, the selection unit 34 identifies line structure images DS2, DS4, and DS6 as the maximum three line structure images with the largest feature values ​​for the pixel of interest. The selection unit 34 then selects tomographic images D2, D4, and D6 corresponding to the identified line structure images as tomographic images including line structures for the pixel of interest. Note that the selection unit 34 may select a maximum of three tomographic images with feature values ​​equal to or greater than a predetermined threshold value Th6 as tomographic images including line structures.

[0108] The number of selected tomographic images Dj including line structures is not limited to a maximum of three. For example, for each corresponding pixel between the tomographic images Dj, only one tomographic image having the maximum feature value in the line structure image DSj may be selected as the tomographic image including line structures. Also, all tomographic images having feature values ​​equal to or greater than a predetermined threshold value Th7 in the line structure image DSj may be selected as the tomographic images including line structures. Also, when the number of tomographic images having feature values ​​greater than 0 among the six tomographic images D1 to D6 is less than three, the selection unit 34 may select one or two tomographic images having feature values ​​greater than 0 as the tomographic images including line structures.

[0109] Furthermore, when the feature amounts in the line structure images DSj are all 0 for each corresponding pixel among the tomographic images Dj, the selector 34 selects all the tomographic images Dj as predetermined tomographic images.

[0110] Fig. 16 is a diagram for explaining selection of a tomographic image using a feature amount in the second embodiment. In Fig. 16, selection of a tomographic image from six tomographic images D1 to D6 shown in Fig. 7 will be explained. In Fig. 16, the tomographic images D1 to D6 are shown one-dimensionally. The illustration method in Fig. 16 is the same as that in Fig. 10, except that the tomographic images D1 to D6 are used.

[0111] For pixel P1, the feature amount of the line structure is derived in the tomographic images D3 and D4. In this case, for pixel P1, the selection unit 34 selects two tomographic images D3 and D4 that include the line structure.

[0112] For pixel P2, feature amounts of line structures are derived from cross-sectional images D1 to D5, and the cross-sectional images having the top three feature amounts are cross-sectional images D2 to D4. Therefore, for pixel P2, the selection unit 34 selects the three cross-sectional images D2 to D4 that include line structures.

[0113] For pixel P3, the feature amounts of the line structures are derived from the tomographic images D1 to D5, and the tomographic images having the top three magnitudes of the feature amounts are the tomographic images D1 to D3. Therefore, for pixel P3, the selection unit 34 selects the three tomographic images D1 to D3 that include the line structures.

[0114] For pixel P4, feature amounts of line structures are derived from the tomographic images D1 to D5, and the tomographic images having the top three feature amounts are the tomographic images D2 to D4. Therefore, for pixel P4, the selection unit 34 selects the three tomographic images D2 to D4 that include line structures.

[0115] For pixel P5, the feature amounts of the line structures are derived in the three tomographic images D2 to D4, so the selector 34 selects the three tomographic images D2 to D4 that include the line structures for pixel P5.

[0116] For pixels P6 and P7, no feature amount of the line structure is derived in any of the tomographic images D1 to D6. Therefore, for pixels P6 and P7, the selection unit 34 selects all of the tomographic images D1 to D6 as predetermined tomographic images.

[0117] For pixel P8, the feature amounts of the line structures are derived in the three tomographic images D1 to D3. Therefore, for pixel P8, the selection unit 34 selects the three tomographic images D1 to D3 that include the line structures.

[0118] At pixels P9 and P10, line structure feature amounts are derived from the tomographic images D1 to D4, and the tomographic images having the top three feature amounts at both pixels P9 and P10 are the tomographic images D1 to D3. Therefore, at pixels P9 and P10, the selection unit 34 selects the three tomographic images D1 to D3 that include line structures.

[0119] For pixels P11 to P13, and P15, no feature amount of the line structure is derived in any of the tomographic images D1 to D6. Therefore, for pixels P11 to P13, and P15, the selection unit 34 selects all of the tomographic images D1 to D6 as predetermined tomographic images.

[0120] For pixel P14, the feature amount of the line structure is derived in the tomographic images D2 and D3. Therefore, for pixel P14, the selection unit 34 selects two tomographic images D2 and D3 that include the line structure.

[0121] In the second embodiment, the synthesis unit 35 derives a synthetic two-dimensional image CG2 based on the selected tomographic images.

[0122] For pixel P1, the tomographic images D3 and D4 are selected. Therefore, the synthesis unit 35 derives a weighted average of the pixel values ​​of pixel P1 in the tomographic images D3 and D4 according to the feature amounts derived for the corresponding line structure images DS3 and DS4, and sets the weighted average as the pixel value of pixel P1 in the synthesized two-dimensional image CG2. Note that the weight coefficient for the weighted average is derived so that the larger the feature amount is, the larger the value becomes. Also, instead of the weighted average, an additive average value may be used. The same applies to the following explanation.

[0123] For pixels P2, P4, and P5, the tomographic images D2 to D4 are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of pixels P2, P4, and P5 in the tomographic images D2 to D4 according to the feature amount, and sets the weighted average value as the pixel value of pixels P2, P4, and P5 in the synthesized two-dimensional image CG2.

[0124] For pixels P3, and P8 to P10, the tomographic images D1 to D3 are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of the pixels P3, and P8 to P10 in the tomographic images D1 to D3 according to the feature amount, and sets the weighted average value as the pixel value of the pixels P3, and P8 to P10 in the synthesized two-dimensional image CG2.

[0125] For pixels P6, P7, P11 to P13, and P15, all of the tomographic images D1 to D6 are selected. Therefore, the synthesis unit 35 derives the arithmetic average of the pixel values ​​of pixels P6, P7, P11 to P13, and P15 in the tomographic images D1 to D6, and sets the arithmetic average as the pixel value of pixels P6, P7, P11 to P13, and P15 in the synthesized two-dimensional image CG2.

[0126] At pixel P14 、 Two tomographic images D2 and D3 are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of pixel P14 in the tomographic images D2 and D3 according to the feature amount, and sets the weighted average value as the pixel value of pixel P14 in the synthesized two-dimensional image CG2.

[0127] The display control unit 36 ​​displays the composite two-dimensional image CG2 generated by the synthesis unit 35 on the display 24. FIG. 17 is a diagram showing a display screen of the composite two-dimensional image in the second embodiment. As shown in FIG. 17, the composite two-dimensional image CG2 is displayed on the display screen 50 of the display 24. Note that the composite two-dimensional image CG2 shown in FIG. 17 is generated from the tomographic image Dj shown in FIG. 7. Note that in FIG. 17, not all of the reference numbers are given to the structures included in the tomographic image shown in FIG. 7. The composite two-dimensional image CG2 shown in FIG. 17 includes the line structures, calcifications, and low-frequency structures included in the tomographic image Dj.

[0128] Comparing the composite two-dimensional image CG2 shown in Fig. 17 with the composite two-dimensional image CG1 according to the first embodiment shown in Fig. 13, in the composite two-dimensional image CG2, the calcification K11 included in the tomographic image D1 overlaps with the line structure K21 included in the tomographic image D2, and the calcification K61 included in the tomographic image D6 overlaps with the line structure K51 included in the tomographic image D5.

[0129] Next, the process performed in the second embodiment will be described. FIG. 18 is a flowchart showing the process performed in the second embodiment. It is assumed that the plurality of tomographic images Dj are acquired in advance and stored in the storage 23. When the input device 25 receives an instruction from the operator to start the process, the process starts, and the lineation image derivation unit 31 derives a lineation image DSj from the plurality of tomographic images Dj (step ST11). Next, the feature derivation unit 32 derives a feature representing the feature of the lineation from each of the plurality of lineation images DSj (step ST12).

[0130] Next, the selection unit 34 selects a tomographic image for each corresponding pixel in each of the tomographic images Dj based on the feature amount (step ST13). That is, the selection unit 34 selects at least one tomographic image including a line structure or a predetermined tomographic image for each corresponding pixel in each of the tomographic images Dj based on the feature amount.

[0131] Furthermore, the synthesis unit 35 derives a synthetic two-dimensional image CG2 based on the selected tomographic images (step ST14), and the display control unit 36 ​​displays the synthetic two-dimensional image CG2 on the display 24 (step ST15), thereby completing the process.

[0132] The image processing device 4A according to the second embodiment may be provided with a structure of interest detection unit 33, as in the first embodiment. In this case, the selection unit 34 selects a tomographic image Dj used to generate a composite two-dimensional image CG2, based on the feature amount derived by the feature amount derivation unit 32 and the calcification detected by the structure of interest detection unit 33 for each corresponding pixel of the tomographic image Dj.

[0133] In the second embodiment, the selection unit 34 selects the tomographic images Dj, and the synthesis unit 35 synthesizes the tomographic images Dj to derive the synthetic two-dimensional image CG2, but the present invention is not limited to this. As in the first embodiment, the selection unit 34 may select the high-frequency tomographic images DHj used to generate the synthetic two-dimensional image based on the feature amount derived by the feature amount derivation unit 32. In this case, the synthesis unit 35 may derive a synthetic high-frequency image (GH2) and a synthetic low-frequency image (GL2) as in the first embodiment, and synthesize the synthetic high-frequency image GH2 and the synthetic low-frequency image GL2 to derive the synthetic two-dimensional image CG2.

[0134] Next, a third embodiment of the present disclosure will be described. The functional configuration of the image processing device according to the third embodiment is the same as the functional configuration of the image processing device 4 according to the first embodiment, and only the processing performed is different, so a detailed description of the device will be omitted here. In the third embodiment, compared to the first embodiment, the processing performed in the line structure image derivation unit 31, the feature amount derivation unit 32, the selection unit 34, and the synthesis unit 35 is different from the first embodiment.

[0135] In the third embodiment, the line structure image derivation unit 31 performs filtering processing on the tomographic image Dj using a high-pass filter to derive a high-frequency tomographic image DHj representing the high-frequency components of the tomographic image Dj. Also, the line structure image derivation unit 31 performs filtering processing on the tomographic image Dj using a low-pass filter to derive a low-frequency tomographic image DLj representing the low-frequency components of the tomographic image Dj. Then, the line structure image derivation unit 31 emphasizes the high-frequency components of the tomographic image Dj based on the high-frequency tomographic image DHj. Specifically, the line structure image derivation unit 31 emphasizes the high-frequency components of the tomographic image Dj by multiplying the pixel value of the pixel of the tomographic image Dj corresponding to the pixel whose pixel value of the high-frequency tomographic image DHj is equal to or greater than a threshold value by a4 (a4>1) or adding a constant value to the pixel value. Also, the line structure image derivation unit 31 suppresses the low-frequency components of the tomographic image Dj whose high-frequency components have been emphasized based on the low-frequency tomographic image DLj. Specifically, the pixel value of the pixel in the tomographic image Dj corresponding to the pixel whose pixel value in the low-frequency tomographic image DLj is equal to or greater than a threshold value is multiplied by a5 (a5<1) or a constant value is subtracted from it, thereby suppressing the low-frequency components of the tomographic image Dj in which the high-frequency components are emphasized.

[0136] The line structure image derivation unit 31 extracts high-frequency line structures and derives a line structure image DSj by applying a directional filter to the tomographic image Dj in accordance with the direction of pixel connections of the tomographic image Dj in which high-frequency components are emphasized and low-frequency components are suppressed. In the third embodiment, the line structure image DSj may be derived by only emphasizing the high-frequency components of the tomographic image Dj. In this case, the derivation of the low-frequency tomographic image DLj is unnecessary. In the third embodiment, the line structure image DSj may be derived by only suppressing the low-frequency components of the tomographic image Dj. In this case, the derivation of the high-frequency tomographic image DHj is unnecessary.

[0137] In the third embodiment, the selection unit 34 selects the tomographic images Dj to be used for generating the composite two-dimensional image based on the feature amount derived by the feature amount derivation unit 32 and the calcification detected by the structure of interest detection unit 33 for each corresponding pixel of the tomographic images Dj. At this time, the selection unit 34 first selects the tomographic images Dj based on the calcification detected by the structure of interest detection unit 33. That is, when calcification is detected in corresponding pixels between the tomographic images Dj, in the third embodiment, the selection unit 34 first selects the tomographic images Dj in which the calcification is detected. At this time, as in the first embodiment, a predetermined number (for example, up to three) of tomographic images Dj in which the variance value when the calcification is detected is equal to or greater than the threshold value Th8 is selected. Note that all the tomographic images Dj in which the calcification is detected may be selected. Alternatively, one tomographic image Dj in which the variance value when the calcification is detected is maximum may be selected.

[0138] For example, when calcification is detected in the tomographic images D2 and D3 for the corresponding pixels of interest in six tomographic images D1 to D6, the selector 34 selects the tomographic images D2 and D3 as the tomographic images to be used for generating the composite two-dimensional image for the pixel of interest. When calcification is detected in more than a predetermined number of tomographic images for the pixel of interest, the selector 34 may select the tomographic images in which the variance value at the time of detecting the calcification is the highest predetermined number.

[0139] In the third embodiment, the selector 34 selects at least one tomographic image including a line structure or a predetermined tomographic image for each corresponding pixel between the tomographic images Dj for which no calcification is detected, based on the feature amount. The selection of at least one tomographic image including a line structure or a predetermined tomographic image is performed in the same manner as in the second embodiment.

[0140] Fig. 19 is a diagram for explaining the selection of a tomographic image in the third embodiment. In Fig. 19, the selection of a tomographic image from the six tomographic images D1 to D6 shown in Fig. 7 will be explained. In Fig. 19, the tomographic images D1 to D6 are shown one-dimensionally. The illustration method in Fig. 19 is the same as that in Fig. 10 except that the tomographic images D1 to D6 are used.

[0141] As shown in FIG. 19, calcification is detected in pixels P4, P6, P9, and P14. In pixel P4, calcification is detected in tomographic images D3 and D4. Therefore, the selection unit 34 selects the tomographic images D3 and D4 in pixel P4. In pixel P6, calcification is detected in tomographic image D2. Therefore, the selection unit 34 selects the tomographic image D2 in pixel P6. In pixel P9, calcification is detected in tomographic images D4 to D6. Therefore, the selection unit 34 selects the tomographic images D4 to D6 in pixel P9. In pixel P14, calcification is detected in tomographic images D3 to D5. Therefore, the selection unit 34 selects the tomographic images D3 to D5 in pixel P14.

[0142] Then, for pixels P1 to P3, P5, P7, P8, P10 to P13, and P15 in which calcification is not detected, the selection unit 34 selects a tomographic image Dj based on the features derived by the feature derivation unit 32 for each corresponding pixel in the tomographic image Dj.

[0143] Next, pixels P1 to P3, P5, P7, P8, P10 to P13, and P15 in which no calcification was detected will be described.

[0144] For pixel P1, the feature amount of the line structure is derived in the tomographic images D3 and D4. In this case, for pixel P1, the selection unit 34 selects two tomographic images D3 and D4 that include the line structure.

[0145] For pixel P2, feature amounts of line structures are derived from cross-sectional images D1 to D5, and the cross-sectional images having the top three feature amounts are cross-sectional images D2 to D4. Therefore, for pixel P2, the selection unit 34 selects the three cross-sectional images D2 to D4 that include line structures.

[0146] For pixel P3, the feature amounts of the line structures are derived from the tomographic images D1 to D5, and the tomographic images having the top three magnitudes of the feature amounts are the tomographic images D1 to D3. Therefore, for pixel P3, the selection unit 34 selects the three tomographic images D1 to D3 that include the line structures.

[0147] For pixel P5, the feature amounts of the line structures are derived in the three tomographic images D2 to D4. Therefore, for pixel P5, the selection unit 34 selects the three tomographic images D2 to D4 that include the line structures.

[0148] At pixel P7, the feature amount of the line structure is not derived in any of the tomographic images D1 to D6. Therefore, at pixel P7, the selection unit 34 selects all of the tomographic images D1 to D6 as the predetermined tomographic images.

[0149] For pixel P8, the feature amounts of the line structures are derived in the three tomographic images D1 to D3. Therefore, for pixel P8, the selection unit 34 selects the three tomographic images D1 to D3 that include the line structures.

[0150] For pixel P10, the feature amounts of the line structures are derived from the tomographic images D1 to D4, and the tomographic images having the top three magnitudes of the feature amounts are the tomographic images D1 to D3. Therefore, for pixel P10, the selection unit 34 selects the three tomographic images D1 to D3 that include the line structures.

[0151] For pixels P11 to P13, and P15, no feature amount of the line structure is derived in any of the tomographic images D1 to D6. Therefore, for pixels P11 to P13, and P15, the selection unit 34 selects all of the tomographic images D1 to D6 as predetermined tomographic images.

[0152] In the third embodiment, the synthesis unit 35 derives a synthetic two-dimensional image CG3 based on a selected tomographic image in areas of line structures and calcifications, and based on a predetermined tomographic image in areas other than line structures and calcifications.

[0153] For pixel P1, the tomographic images D3 and D4 are selected. Therefore, the synthesis unit 35 derives a weighted average of the pixel values ​​of pixel P1 in the tomographic images D3 and D4 according to the feature amounts derived for the corresponding line structure images DS3 and DS4, and sets the weighted average as the pixel value of pixel P1 in the synthesized two-dimensional image CG3. Note that the weight coefficient for the weighted average is derived so that the larger the feature amount is, the larger the value becomes. Also, instead of the weighted average, an additive average value may be used. The same applies to the following explanation.

[0154] For pixels P2 and P5, the tomographic images D2 to D4 are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of pixels P2 and P5 in the tomographic images D2 to D4 according to the feature amount, and sets the weighted average value as the pixel value of pixels P2 and P5 in the synthesized two-dimensional image CG3.

[0155] For pixels P3, P8, and P10, the tomographic images D1 to D3 are selected. Therefore, the synthesis unit 35 derives a weighted average value of the pixel values ​​of pixels P3, P8, and P10 in the tomographic images D1 to D3 according to the feature amount, and sets the weighted average value as the pixel value of pixels P3, P8, and P10 in the synthesized two-dimensional image CG3.

[0156] For pixel P4, the tomographic images D3 and D4 are selected. Therefore, the synthesis unit 35 derives a weighted average of the pixel values ​​of pixel P4 in the tomographic images D3 and D4 according to the variance value used in detecting calcification, and sets the weighted average as the pixel value of pixel P4 in the synthesized two-dimensional image CG3.

[0157] For pixel P6, the tomographic image D2 is selected. Therefore, the synthesis unit 35 sets the pixel value of pixel P6 in the tomographic image D2 as the pixel value of pixel P6 in the synthesized two-dimensional image CG3.

[0158] For pixels P7, P11 to P13, and P15, all of the tomographic images D1 to D6 are selected. Therefore, the synthesis unit 35 derives the arithmetic average of the pixel values ​​of pixels P7, P11 to P13, and P15 in the tomographic images D1 to D6, and sets the arithmetic average as the pixel value of pixels P7, P11 to P13, and P15 in the synthesized two-dimensional image CG3.

[0159] For pixel P9, the tomographic images D4 to D6 are selected. Therefore, the synthesis unit 35 derives a weighted average of the pixel values ​​of pixel P9 in the tomographic images D4 to D6 according to the variance value used in detecting calcification, and sets the weighted average as the pixel value of pixel P9 in the synthesized two-dimensional image CG3.

[0160] For pixel P14, the tomographic images D3 to D5 are selected. Therefore, the synthesis unit 35 derives a weighted average of the pixel values ​​of pixel P14 in the tomographic images D3 to D6 according to the variance value used in detecting calcification, and sets the weighted average as the pixel value of pixel P14 in the synthesized two-dimensional image CG3.

[0161] It should be noted that the composite two-dimensional image CG3 derived in the third embodiment is substantially the same as the composite two-dimensional image CG1 derived in the first embodiment.

[0162] Next, the process performed in the third embodiment will be described. FIG. 20 is a flow chart showing the process performed in the third embodiment. It is assumed that the plurality of tomographic images Dj are acquired in advance and stored in the storage 23. When the input device 25 receives an instruction from the operator to start the process, the process starts, and the lineation image derivation unit 31 derives a lineation image DSj from the plurality of tomographic images Dj (step ST21). Next, the feature derivation unit 32 derives a feature representing the feature of the lineation from each of the plurality of lineation images DSj (step ST22). In addition, the structure of interest detection unit 33 detects calcification as a structure of interest from each of the plurality of tomographic images Dj (step ST23).

[0163] Next, the selection unit 34 selects at least one tomographic image including calcification for each corresponding pixel in each of the tomographic images Dj based on the detected calcification (step ST24). The selection unit 34 also selects a tomographic image for each corresponding pixel in each of the tomographic images Dj based on the feature amount (step ST25). That is, the selection unit 34 selects at least one tomographic image including a line structure or a predetermined tomographic image for each corresponding pixel in each of the tomographic images Dj based on the feature amount.

[0164] Furthermore, the synthesis unit 35 derives a synthetic two-dimensional image CG3 based on the selected tomographic images (step ST26), and the display control unit 36 ​​displays the synthetic two-dimensional image CG3 on the display 24 (step ST27), thereby completing the process.

[0165] In the first embodiment, the structure of interest detection unit 33 detects calcification as a structure of interest from the tomographic image Dj, and selects a tomographic image based on the calcification, but the present invention is not limited to this. In the first embodiment, the structure of interest detection unit 33 may not be provided, and the high-frequency tomographic image DHj may be selected based only on the feature amount.

[0166] In the first embodiment, the feature amount derivation section 32 may derive the feature amount in the same manner as in the second or third embodiment.

[0167] In the first and second embodiments, the line structure image derivation unit 31 may derive a line structure image in the same manner as in the third embodiment.

[0168] In the first embodiment, a composite high frequency image may be derived in advance by calculating a weighted average value of pixel values ​​of corresponding pixels for the high frequency tomographic images DHj. The composite high frequency image derived in advance is called a pre-composite high frequency image. In this case, the selection unit 34 selects only high frequency tomographic images including line structures or high frequency tomographic images in which calcification is detected. In this case, the composition unit 35 uses the pre-composite high frequency image to derive a composite high frequency image GH1.

[0169] Specifically, among the pixels of the high frequency tomographic image DHj, the pixels in which the high frequency tomographic image including the line structure is selected and the pixels in which the calcification is detected are High frequency tomographic images were selected For pixels, the pixel values ​​of the composite high frequency image GH1 are derived using the high frequency tomographic image including line structures and the high frequency tomographic image in which calcification has been detected. On the other hand, for pixels for which the high frequency tomographic image including line structures or the high frequency tomographic image in which calcification has been detected has not been selected, the pixel value of the corresponding pixel in the pre-synthesized high frequency image is set as the pixel value of the composite high frequency image GH1. Note that the pixel values ​​of the composite high frequency image GH1 may be derived using the high frequency tomographic image including line structures and the high frequency tomographic image in which calcification has been detected, and the derived pixel values ​​may be added to the pre-synthesized high frequency image to derive the composite high frequency image GH1.

[0170] In the second and third embodiments, a composite two-dimensional image may be derived in advance by calculating a weighted average value of pixel values ​​of corresponding pixels for the tomographic images Dj. The composite two-dimensional image derived in advance is called a pre-composite image. In this case, the selection unit 34 selects only tomographic images including line structures or tomographic images in which calcification is detected. In this case, the composition unit 35 uses the pre-composite image to derive composite two-dimensional images CG2 and CG3.

[0171] Specifically, for pixels of the tomographic image Dj for which a tomographic image including line structures and a tomographic image in which calcification has been detected have been selected, the pixel values ​​of the composite two-dimensional images CG2 and CG3 are derived using the tomographic image including line structures and the tomographic image in which calcification has been detected. On the other hand, for pixels for which a tomographic image including line structures or a tomographic image in which calcification has been detected has not been selected, the pixel values ​​of the corresponding pixels of the pre-composite image are set as the pixel values ​​of the composite two-dimensional images CG2 and CG3. The pixel values ​​of the composite two-dimensional images CG2 and CG3 may be derived using the tomographic image including line structures and the tomographic image in which calcification has been detected, and the derived pixel values ​​may be added to the pre-composite images to derive the composite two-dimensional images CG2 and CG3.

[0172] Moreover, the radiation in each of the above embodiments is not particularly limited, and in addition to X-rays, α-rays, γ-rays, etc. can be used.

[0173] In each of the above embodiments, the following various processors can be used as the hardware structure of the processing unit that executes various processes, such as the image acquisition unit 30, the line structure image derivation unit 31, the feature amount derivation unit 32, the structure of interest detection unit 33, the selection unit 34, the synthesis unit 35, and the display control unit 36. As described above, the above various processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as various processing units, as well as a programmable logic device (PLD), which is a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field Programmable Gate Array), and a dedicated electric circuit, which is a processor having a circuit configuration designed specifically for executing specific processes, such as an ASIC (Application Specific Integrated Circuit).

[0174] A single processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs or a combination of a CPU and an FPGA). Also, multiple processing units may be configured with a single processor.

[0175] As an example of configuring multiple processing units with one processor, first, there is a form in which one processor is configured with a combination of one or more CPUs and software, as typified by computers such as client and server, and this processor functions as multiple processing units. Second, there is a form in which a processor is used that realizes the functions of the entire system including multiple processing units with one IC (Integrated Circuit) chip, as typified by System On Chip (SoC), etc. In this way, the various processing units are configured using one or more of the above various processors as a hardware structure.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. [Explanation of symbols]

[0177] 1. Mammography equipment 2 Console 3. Image storage system 4,4A Image processing device 11 Rotation axis 12 Arm section 13. Shooting stand 14 Radiation Irradiation Section 15 Radiation detector 15A Detection surface 16 Radiation source 17 Compression plate 21 CPU 22 Image Processing Programs 23. Storage 24 Display 25 Input Devices 26 Memory 27 Network Interface 28 Bus 30 Image acquisition unit 31 Linear structure image extraction section 32 Feature extraction unit 33 Interesting Structure Detection Unit 34 Selection section 35 Synthesis section 36 Display control section 50 display screen 100 Radiography system CG1~CG3 Composite 2D images Dj(j=1~m), Dj tomographic image DLj Low frequency tomographic image DHj High frequency tomographic image DSj Lineation Image Gi(i=1~n) Projection image K11,K61 Calcification K21~K23, K31, K32, K41, K42, K51, K52 Linear structure K23, K33, K43, K53 Low frequency structure M Breast P1~P15 pixels Si(i=1~n) Source position Sc reference source position X0 optical axis

Claims

1. At least one processor; The processor, A line structure image representing a high frequency line structure is derived from each of a plurality of tomographic images representing a tomographic plane of the subject; Deriving a feature quantity representing a feature of the line structure from the line structure image; selecting, for each corresponding pixel in each of the tomographic images or high-frequency tomographic images representing high-frequency components of the tomographic images, a set of candidate images including at least one of the tomographic images or high-frequency tomographic images including the line structure, or a predetermined tomographic image or high-frequency tomographic image, based on the feature amount; An image processing device configured to derive a composite two-dimensional image by calculating a weighted average value according to the feature amount for images included in the selected set of candidate images.

2. The processor selects at least one of the tomographic images or the high frequency tomographic images that includes the line structure; 2. The image processing device according to claim 1, configured to derive the composite two-dimensional image by calculating a weighted average value according to the feature amount for a pre-composite image generated in advance based on the tomographic image or the high-frequency tomographic image, and for images included in the set of candidate images including the selected tomographic image or high-frequency tomographic image.

3. The processor detects a predetermined structure of interest from the tomographic image or the high frequency tomographic image; 3. The image processing device according to claim 1 or 2, wherein, for a corresponding pixel of the tomographic image or the high-frequency tomographic image in which the structure of interest is detected, the tomographic image or the high-frequency tomographic image in which the structure of interest is detected is selected as an image to be included in the set of candidate images, instead of the tomographic image or the high-frequency tomographic image including the line structure or the predetermined tomographic image or the high-frequency tomographic image.

4. The image processing apparatus of claim 3 , wherein the object is a breast and the structure of interest is a calcification.

5. The image processing device according to claim 1 , wherein the subject is a breast, and the linear structures are mammary glands and spicules.

6. The image processing device according to claim 1 , wherein the processor is configured to derive pixel values ​​of the line structure image as the feature amount.

7. The image processing device according to claim 1 , wherein the processor is configured to derive a variance value for each pixel of the line structure image as the feature amount.

8. The image processing device according to claim 1 , wherein the processor is configured to derive the feature amount by converting pixel values ​​of the line structure image.

9. The image processing device according to claim 1 , wherein the processor is configured to select the tomographic image or the high-frequency tomographic image including the line structure whose feature amount is in the top 1 or higher as an image to be included in the set of candidate images.

10. The image processing device according to any one of claims 1 to 8, wherein the processor is configured to select the tomographic image or the high-frequency tomographic image including the line structure whose feature amount is equal to or greater than a predetermined threshold value as an image to be included in the set of candidate images.

11. A line structure image representing a high frequency line structure is derived from each of a plurality of tomographic images representing a tomographic plane of the subject; Deriving a feature quantity representing a feature of the line structure from the line structure image; selecting, for each corresponding pixel in each of the tomographic images or high-frequency tomographic images representing high-frequency components of the tomographic images based on the feature amount, a set of candidate images including at least one of the tomographic images or high-frequency tomographic images including the line structure, or a predetermined tomographic image or high-frequency tomographic image, based on the feature amount; An image processing method for deriving a composite two-dimensional image by calculating a weighted average value according to the feature amount for images included in the selected set of candidate images.

12. A step of deriving a line structure image representing a high frequency line structure from each of a plurality of tomographic images representing a tomographic plane of a subject; A step of deriving a feature quantity representing a feature of the line structure from the line structure image; selecting, for each corresponding pixel in each of the tomographic images or high-frequency tomographic images representing high-frequency components of the tomographic images based on the feature amount, a set of candidate images including at least one of the tomographic images or high-frequency tomographic images including the line structure, or a predetermined tomographic image or high-frequency tomographic image, based on the feature amount; and a step of deriving a composite two-dimensional image by calculating a weighted average value according to the feature amount for the images included in the selected set of candidate images.

Images