Patent Publication Number: US-2023146430-A1

Title: Image processing device, image processing method, and image processing program

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation application of International Application No. PCT/JP2021/018613 filed May 17, 2021 the disclosure of which is incorporated herein by reference in its entirety. Further, this application claims priorities from Japanese Patent Application No. 2020-125752, filed Jul. 22, 2020, the disclosure of which is incorporated herein by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to an image processing device, an image processing method, and an image processing program. 
     RELATED ART 
     In recent years, image diagnosis using a radiography apparatus (called mammography) for capturing an image of a breast has attracted attention in order to promote early detection of breast cancer. Further, in the mammography, tomosynthesis imaging has been proposed which moves a radiation source, irradiates the breast with radiation at a plurality of radiation source positions to acquire a plurality of projection images, and reconstructs the plurality of acquired projection images to generate tomographic images in which desired tomographic planes have been highlighted. In the tomosynthesis imaging, the radiation source is moved in parallel to a radiation detector or is moved so as to draw a circular or elliptical arc according to the characteristics of an imaging apparatus and the required tomographic image, and imaging is performed on the breast at a plurality of radiation source positions to acquire a plurality of projection images. Then, the projection images are reconstructed using, for example, a back projection method, such as a simple back projection method or a filtered back projection method, or a sequential reconstruction method to generate tomographic images. 
     The tomographic images are generated in a plurality of tomographic planes of the breast, which makes it possible to separate structures that overlap each other in a depth direction in which the tomographic planes are arranged in the breast. Therefore, it is possible to find an abnormal part such as a lesion that has been difficult to detect in a two-dimensional image (hereinafter, referred to as a simple two-dimensional image) acquired by simple imaging according to the related art which irradiates an object with radiation in a predetermined direction. 
     In addition, a technique has been known which combines a plurality of tomographic images having different distances (positions in a height direction) from a detection surface of a radiation detector to a radiation source, which have been acquired by tomosynthesis imaging, using, for example, an addition method, an averaging method, a maximum intensity projection method, or a minimum intensity projection method to generate a pseudo two-dimensional image (hereinafter, referred to as a composite two-dimensional image) corresponding to the simple two-dimensional image (see JP2014-128716A). 
     In contrast, in the medical field, a computer aided diagnosis (hereinafter, referred to as CAD) system has been known which automatically detects a structure, such as an abnormal shadow, in an image and displays the detected structure so as to be highlighted. For example, the CAD is used to detect important diagnostic structures, such as a tumor, a spicula, and a calcification, from the tomographic images acquired by the tomosynthesis imaging. In addition, a method has been proposed which, in a case in which a composite two-dimensional image is generated from a plurality of tomographic images acquired by performing the tomosynthesis imaging on the breast, detects a region of interest including a structure using the CAD and combines the detected region of interest on, for example, a projection image or a two-dimensional image acquired by simple imaging to generate a composite two-dimensional image (see the specification of U.S. Pat. No. 8,983,156B). Further, a method has been proposed which averages and combines tomographic images including only the structure detected by the CAD to generate a composite two-dimensional image (see the specification of U.S. Pat. No. 9,792,703B). 
     However, in the composite two-dimensional image generated by the method disclosed in the specification of U.S. Pat. No. 8,983,156B, the structure of interest combined with the two-dimensional image is only the structure of interest acquired from one tomographic image. Therefore, in a case in which linear structures including light and thin lines, such as a mammary gland structure and a spicula in the breast, is present across a plurality of tomographic images, it is not possible to reflect a state in which the structure is present in a depth direction in which the tomographic images are arranged in the composite two-dimensional image. In addition, the method disclosed in the specification of U.S. Pat. No. 9,792,703B averages the structures of interest included in a plurality of tomographic images. Therefore, for example, a fine structure of interest, such as a calcification, and a linear structure, such as a mammary gland or a spicula, included in the breast are faint and difficult to see. 
     SUMMARY 
     The present invention has been made in view of the above circumstances, and an object of the present invention is to make it easy to see a fine structure included in an object in a composite two-dimensional image. 
     According to the present disclosure, there is provided an image processing device comprising at least one processor. The processor is configured to derive a linear structure image indicating a high-frequency linear structure from each of a plurality of tomographic images indicating tomographic planes of an object, to derive a feature amount indicating features of the linear structure from the linear structure image, to select at least one tomographic image or high-frequency tomographic image including the linear structure or a predetermined tomographic image or high-frequency tomographic image on the basis of the feature amount for each corresponding pixel in each of the tomographic images or the high-frequency tomographic images indicating high-frequency components of the tomographic images, and to derive a composite two-dimensional image on the basis of the selected tomographic images or high-frequency tomographic images. 
     In addition, in the image processing device according to the present disclosure, the processor may be configured to select the at least one tomographic image or high-frequency tomographic image including the linear structure and to derive the composite two-dimensional image on the basis of a pre-composite image generated in advance on the basis of the tomographic images or the high-frequency tomographic images and the selected tomographic images or high-frequency tomographic images. 
     Further, in the image processing device according to the present disclosure, the processor may be configured to detect a predetermined structure of interest from the tomographic image or the high-frequency tomographic image, and to select the tomographic image or the high-frequency tomographic image from which the structure of interest has been detected, in a corresponding pixel in the tomographic images or the high-frequency tomographic images from which the structure of interest has been detected, instead of the tomographic image or the high-frequency tomographic image including the linear structure, or the predetermined tomographic image or high-frequency tomographic image. 
     In addition, in the image processing device according to the present disclosure, the object may be a breast, and the structure of interest may be a calcification. 
     Further, in the image processing device according to the present disclosure, the object may be a breast, and the linear structure may be a mammary gland and a spicula. 
     Furthermore, in the image processing device according to the present disclosure, the processor may be configured to derive a pixel value of the linear structure image as the feature amount. 
     In addition, in the image processing device according to the present disclosure, the processor may be configured to derive a variance value of each pixel of the linear structure image as the feature amount. 
     Further, in the image processing device according to the present disclosure, the processor may be configured to convert a pixel value of the linear structure image to derive the feature amount. 
     Furthermore, in the image processing device according to the present disclosure, the processor may be configured to select the tomographic image or the high-frequency tomographic image that includes the linear structure having at least a largest feature amount. 
     Moreover, in the image processing device according to the present disclosure, the processor may be configured to select the tomographic image or the high-frequency tomographic image that includes the linear structure having the feature amount equal to or greater than a predetermined threshold value. 
     In addition, according to the present disclosure, there is provided an image processing method comprising: deriving a linear structure image indicating a high-frequency linear structure from each of a plurality of tomographic images indicating tomographic planes of an object; deriving a feature amount indicating features of the linear structure from the linear structure image; selecting at least one tomographic image or high-frequency tomographic image including the linear structure or a predetermined tomographic image or high-frequency tomographic image on the basis of the feature amount for each corresponding pixel in each of the tomographic images or the high-frequency tomographic images indicating high-frequency components of the tomographic images; and deriving a composite two-dimensional image on the basis of the selected tomographic images or high-frequency tomographic images. 
     In addition, a program that causes a computer to perform the image processing method according to the present disclosure may be provided. 
     According to the present disclosure, it is possible to easily see a fine structure included in an object in a composite two-dimensional image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram schematically illustrating a configuration of a radiography system to which an image processing device according to a first embodiment of the present disclosure is applied. 
         FIG.  2    is a diagram illustrating a radiography apparatus as viewed from a direction of an arrow A in  FIG.  1   . 
         FIG.  3    is a diagram schematically illustrating a configuration of the image processing device according to a first embodiment. 
         FIG.  4    is a diagram illustrating a functional configuration of the image processing device according to the first embodiment. 
         FIG.  5    is a diagram illustrating the acquisition of projection images. 
         FIG.  6    is a diagram illustrating the generation of tomographic images. 
         FIG.  7    is a diagram illustrating an example of the tomographic images. 
         FIG.  8    is a diagram illustrating low-frequency tomographic images and high-frequency tomographic images. 
         FIG.  9    is a diagram illustrating linear structure images. 
         FIG.  10    is a diagram illustrating the selection of the high-frequency tomographic image in the first embodiment. 
         FIG.  11    is a diagram illustrating the generation of a composite high-frequency image. 
         FIG.  12    is a diagram illustrating the generation of a composite low-frequency image. 
         FIG.  13    is a diagram illustrating a composite two-dimensional image display screen in the first embodiment. 
         FIG.  14    is a flowchart illustrating a process performed in the first embodiment. 
         FIG.  15    is a diagram illustrating a functional configuration of an image processing device according to a second embodiment. 
         FIG.  16    is a diagram illustrating the selection of a tomographic image in the second embodiment. 
         FIG.  17    is a diagram illustrating a composite two-dimensional image display screen in the second embodiment. 
         FIG.  18    is a flowchart illustrating a process performed in the second embodiment. 
         FIG.  19    is a diagram illustrating the selection of a tomographic image in a third embodiment. 
         FIG.  20    is a flowchart illustrating a process performed in the third embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.  FIG.  1    is a diagram schematically illustrating a configuration of a radiography system to which an image processing device according to an embodiment of the present disclosure is applied, and  FIG.  2    is a diagram illustrating a mammography apparatus in the radiography system as viewed from a direction of an arrow A in  FIG.  1   . As illustrated in  FIG.  1   , a radiography system  100  according to this embodiment images a breast M, which is an object, at a plurality of radiation source positions and acquires a plurality of radiographic images, that is, a plurality of projection images, in order to perform tomosynthesis imaging on the breast to generate tomographic images. The radiography system  100  according to this embodiment comprises a mammography apparatus  1 , a console  2 , an image storage system  3 , and an image processing device  4 . 
     The mammography apparatus  1  comprises an arm portion  12  that is connected to a base (not illustrated) by a rotation shaft  11 . An imaging table  13  is attached to one end of the arm portion  12 , and a radiation emitting unit  14  is attached to the other end of the arm portion  12  so as to face the imaging table  13 . The arm portion  12  is configured such that only the end to which the radiation emitting unit  14  is attached can be rotated. Therefore, the imaging table  13  is fixed, and only the radiation emitting unit  14  can be rotated. 
     A radiation detector  15 , such as a flat panel detector, is provided in the imaging table  13 . The radiation detector  15  has a detection surface  15 A for radiation. In addition, for example, a circuit substrate including a charge amplifier that converts a charge signal read 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 analog-digital (AD) conversion unit that converts the voltage signal into a digital signal is also provided in the imaging table  13 . 
     A radiation source  16  is accommodated in the radiation emitting unit  14 . The radiation source  16  emits, for example, X-rays as the radiation. The console  2  controls the timing when the radiation source  16  emits the radiation and the radiation generation conditions of the radiation source  16 , that is, the selection of target and filter materials, a tube voltage, an irradiation time, and the like. 
     Further, the arm portion  12  is provided with a compression plate  17  that is disposed above the imaging table  13  and presses and compresses the breast M, a support portion  18  that supports the compression plate  17 , and a movement mechanism  19  that moves the support portion  18  in an up-down direction in  FIGS.  1  and  2   . In addition, an interval between the compression plate  17  and the imaging table  13 , that is, a compression thickness is input to the console  2 . 
     The console  2  has a function of controlling the mammography apparatus  1  using, for example, an imaging order and various kinds of information acquired from a radiology information system (RIS) (not illustrated) or the like through a network, such as a wireless communication local area network (LAN), and instructions or the like directly issued by a radiology technician or the like. Specifically, the console  2  directs the mammography apparatus  1  to perform the tomosynthesis imaging on the breast M, acquires a plurality of projection images as described below, and reconstructs the plurality of projection images to generate a plurality of tomographic images. For example, in this embodiment, a server computer is used as the console  2 . 
     The image storage system  3  is a system that stores image data such as radiographic images and tomographic images captured by the mammography apparatus  1 . The image storage system  3  extracts an image corresponding to a request from, for example, the console  2  and the image processing device  4  from the stored images and transmits the image to a device that is the source of the request. A specific example of the image storage system  3  is a picture archiving and communication system (PACS). 
     Next, an image processing device according to a first embodiment will be described. Next, a hardware configuration of the image processing device according to the first embodiment will be described with reference to  FIG.  3   . As illustrated in  FIG.  3   , the image processing device  4  is a computer, such as a workstation, a server computer, or a personal computer, and comprises a central processing unit (CPU)  21 , a non-volatile storage  23 , and a memory  26  as a temporary storage area. In addition, the image processing device  4  comprises a display  24 , such as a liquid crystal display, an input device  25 , such as a keyboard and a mouse, and a network interface (I/F)  27  that is connected to a network (not illustrated). The CPU  21 , the storage  23 , the display  24 , the input device  25 , the memory  26 , and the network OF  27  are connected to a bus  28 . In addition, the CPU  21  is an example of a processor according to the present disclosure. 
     The storage  23  is implemented by, for example, a hard disk drive (HDD), a solid state drive (SSD), and a flash memory. An image processing program  22  installed in the image processing device  4  is stored in the storage  23  as a storage medium. The CPU  21  reads out the image processing program  22  from the storage  23 , expands the image processing program  22  in the memory  26 , and executes the expanded image processing program  22 . 
     In addition, the image processing program  22  is stored in a storage device of a server computer connected to the network or a network storage in a state in which it can be accessed from the outside and is downloaded and installed in the computer constituting the image processing device  4  as required. Alternatively, the programs are recorded on a recording medium, such as a digital versatile disc (DVD) or a compact disc read only memory (CD-ROM), are distributed, and are installed in the computer constituting the image processing device  4  from the recording medium. 
     Next, a functional configuration of the image processing device according to the first embodiment will be described.  FIG.  4    is a diagram illustrating the functional configuration of the image processing device according to the first embodiment. As illustrated in  FIG.  4   , the image processing device  4  comprises an image acquisition unit  30 , a linear structure image derivation unit  31 , a feature amount derivation unit  32 , a structure-of-interest detection unit  33 , a selection unit  34 , a combination unit  35 , and a display control unit  36 . Then, the CPU  21  executes the image processing program  22  such that the image processing device  4  functions as the image acquisition unit  30 , the linear structure image derivation unit  31 , the feature amount derivation unit  32 , the structure-of-interest detection unit  33 , the selection unit  34 , the combination unit  35 , and the display control unit  36 . 
     The image acquisition unit  30  acquires the tomographic image from the console  2  or the image storage system  3  through the network I/F  27 . 
     Here, the tomosynthesis imaging and the generation of tomographic images in the console  2  will be described. In a case in which the tomosynthesis imaging for generating tomographic images is performed, the console  2  rotates the arm portion  12  about the rotation shaft  11  to move the radiation source  16 , irradiates the breast M, which is an object, with radiation at a plurality of radiation source positions caused by the movement of the radiation source  16  under predetermined imaging conditions for tomosynthesis imaging, detects the radiation transmitted through the breast M using the radiation detector  15 , and acquires a plurality of projection images Gi (i=1 to n, where n is the number of radiation source positions and is, for example,  15 ) at the plurality of radiation source positions. 
       FIG.  5    is a diagram illustrating the acquisition of the projection images Gi. As illustrated in  FIG.  5   , the radiation source  16  is moved to each of radiation source positions S 1 , S 2 , . . . , and Sn. The radiation source  16  is driven at each radiation source position to irradiate the breast M with radiation. The radiation detector  15  detects the radiation transmitted through the breast M to acquire projection images G 1 , G 2 , . . . , and Gn corresponding to the radiation source positions S 1  to Sn, respectively. In addition, at each of the radiation source positions S 1  to Sn, the breast M is irradiated with the same dose of radiation. 
     Furthermore, in  FIG.  5   , a radiation source position Sc is a radiation source position where an optical axis X 0  of the radiation emitted from the radiation source  16  is orthogonal to the detection surface  15 A of the radiation detector  15 . It is assumed that the radiation source position Sc is referred to as a reference radiation source position Sc. 
     Then, the console  2  reconstructs the plurality of projection images Gi to generate tomographic images in which the desired tomographic planes of the breast M have been highlighted. Specifically, the console  2  reconstructs the plurality of projection images Gi using a known back projection method, such as a simple back projection method or a filtered back projection method, to generate a plurality of tomographic images Dj (j=1 to m) in each of a plurality of tomographic planes of the breast M as illustrated in  FIG.  6   . In this case, a three-dimensional coordinate position in a three-dimensional space including the breast M is set, the pixel values of the corresponding pixels in the plurality of projection images Gi are reconstructed for the set three-dimensional coordinate position, and pixel values at the coordinate positions of the pixels are calculated. In addition, in the first embodiment, it is assumed that the pixel values of the tomographic images Dj become larger as brightness becomes higher (that is, closer to white) and become smaller as the brightness becomes lower (that is, closer to black). Further, it is assumed that the pixel value is equal to or greater than 0. 
     The console  2  directly transmits the generated tomographic images Dj to the image processing device  4  or transmits the generated tomographic images Dj to the image storage system  3 . 
     The linear structure image derivation unit  31  derives a linear structure image indicating a high-frequency linear structure from each of a plurality of tomographic images Dj. The high-frequency linear structure in this embodiment is a linear structure with a thickness that is not capable of being clearly expressed in a composite two-dimensional image by the methods disclosed in, for example, U.S. Pat. Nos. 8,983,156B and 9,792,703B. Specifically, the high-frequency linear structure is a linear structure having a thickness of about 200 to 300 μm or less in the structures included in the breast M. Examples of the high-frequency linear structure include a mammary gland and a spicula included in the breast M. 
     Meanwhile, an example of the lesion included in the breast M is a tumor. The tumor has a larger structure than the calcification and the spicula. A structure, such as a tumor, having a larger structure than the calcification and the spicula is referred to as a low-frequency structure in this embodiment. 
       FIG.  7    is a diagram illustrating an example of the tomographic images. In addition,  FIG.  7    illustrates six tomographic images D 1  to D 6 . As illustrated in  FIG.  7   , the tomographic image D 1  includes a calcification K 11 . The tomographic image D 2  includes linear structures K 21  and K 22  and a low-frequency structure K 23  such as a tumor. The tomographic image D 3  includes linear structures K 31  and K 32  and a low-frequency structure K 33 . The tomographic image D 4  includes linear structures K 41  and K 42  and a low-frequency structure K 43 . The tomographic image D 5  includes linear structures K 51  and K 52  and a low-frequency structure K 53 . The tomographic image D 6  includes a calcification K 61 . In addition,  FIG.  7    schematically illustrates various structures included in the breast M in the tomographic images Dj and is different from the actual inclusion of the structures. 
     First, the linear structure image derivation unit  31  derives high-frequency components in each of the plurality of tomographic images Dj in order to derive the linear structure image. Specifically, each of the tomographic images Dj is reduced to derive low-frequency tomographic images DLj indicating low-frequency components of the tomographic images Dj. Then, the low-frequency tomographic images DLj are enlarged to the same size as the original tomographic images Dj, and the enlarged low-frequency tomographic images DLj are subtracted from the original tomographic images Dj to derive high-frequency tomographic images DHj indicating high-frequency components of the tomographic images Dj. In addition, a filtering process using a low-pass filter may be performed on the tomographic image Dj to derive the low-frequency tomographic image DLj, instead of reducing the tomographic image Dj. Further, a filtering process using a high-pass filter may be performed on the tomographic images Dj to derive the high-frequency tomographic images DHj indicating the high-frequency components of the tomographic images Dj. Furthermore, a filtering process using a bandpass filter which extracts a high-frequency component having a thickness of about 300 μm or less in the tomographic image Dj may be performed to derive the high-frequency tomographic image DHj indicating the high-frequency component of the tomographic image Dj. 
       FIG.  8    is a diagram illustrating low-frequency tomographic images and high-frequency tomographic images. In addition,  FIG.  8    illustrates low-frequency tomographic images DL 1  to DL 6  and high-frequency tomographic images DH 1  to DH 6  derived from the tomographic images D 1  to D 6  illustrated in  FIG.  7   . As illustrated in  FIG.  8   , the low-frequency tomographic images DL 1  to DL 6  include only low-frequency structures having a relatively large size such as tumors included in the tomographic images D 1  to D 6 . The high-frequency tomographic images DH 1  to DH 6  include only high-frequency structures having a relatively small size such as a spicula, a mammary gland, and a calcification. 
     In addition, in the low-frequency tomographic images DLj, the low-frequency structure is represented to have high brightness (that is, a large pixel value). Further, in the high-frequency tomographic images DHj, the high-frequency structure is represented to have high brightness. 
     Then, the linear structure image derivation unit  31  applies a directional filter in a direction, in which the pixels of the high-frequency tomographic images DHj are connected, to extract the high-frequency linear structures and derives high-frequency linear structure images DSj. Here, the directional filter is a two-dimensional filter, has a large weight for each of a vertical direction, a horizontal direction, and two diagonal directions in the filter, and smooths the image in other portions. The directional filters are prepared for each of the vertical direction, the horizontal direction, and the two diagonal directions. 
       FIG.  9    is a diagram illustrating linear structure images. In addition,  FIG.  9    illustrates linear structure images DS 1  to DS 6  derived from the high-frequency tomographic images DH 1  to DH 6  illustrated in  FIG.  8   . As illustrated in  FIG.  9   , the linear structure images DS 1  to DS 6  include only linear structures, such as a mammary gland and a spicula, included in the tomographic images Dj. Further, in the linear structure images DSj, linear structures, such as the mammary gland and the spicula, included in the tomographic images Dj are represented to have high brightness (that is, a large pixel value). 
     The linear structure image derivation unit  31  may perform a filtering process using a Sobel filter, a Laplacian filter, or the like on the tomographic images Dj to derive the linear structure images DSj. In addition, the linear structure images DSj may be derived by extracting the linear structures from the tomographic images Dj using CAD. 
     The feature amount derivation unit  32  derives a feature amount indicating the features of the linear structure from the linear structure images DSj. In the first embodiment, a variance value of the pixel value of each pixel in the linear structure images DSj is derived as the feature amount. Specifically, the feature amount derivation unit  32  sets a region of interest with a predetermined size for each pixel of the linear structure images DSj. The size of the region of interest can be, for example, 5×5 pixels. However, the present disclosure is not limited thereto. The region of interest may have any size such as 3×3 pixels or 7×7 pixels. Further, the shape of the region of interest is not limited to a rectangular shape and may be any shape such as a circular shape. 
     The feature amount derivation unit  32  derives a variance value σ 2  of each pixel of the linear structure images DSj as the feature amount indicating the features of the linear structure on the basis of the pixel values of the pixels in the region of interest using the following Expression (1). In Expression (1), r(xi, yi) is the pixel value of each pixel of the linear structure images DSj, rm is an average value of the pixel values in the region of interest, and Σ is the sum of (r(xi, yi)-rm) 2  in the region of interest. 
       σ 2 ( x, y )=Σ( r ( xi, yi )− rm ) 2    (1)
 
     The structure-of-interest detection unit  33  detects the calcification from each of the tomographic images Dj or the high-frequency tomographic images DHj. The calcification is an example of a structure of interest according to the present disclosure. The structure-of-interest detection unit  33  sets the region of interest with a predetermined size for each pixel in order to detect the calcification. The size of the region of interest can be, for example, 5×5 pixels. However, the present disclosure is not limited thereto. The region of interest may have any size such as 3×3 pixels or 7×7 pixels. Further, the shape of the region of interest is not limited to a rectangular shape and may be any shape such as a circular shape. In addition, in the following description, the detection of the calcification from the tomographic images Dj will be described. However, the calcification can also be detected from the high-frequency tomographic images DHj as in the case of the tomographic images Dj. 
     The structure-of-interest detection unit  33  derives a variance value σ 1   2 2  of each pixel of the tomographic images Dj on the basis of the pixel values of the pixels in the region of interest using the following Expression (2). In Expression (2), r 1 (x 1 i, y 1 i) is the pixel value of each pixel of the tomographic images Dj, r 1 m is an average value of the pixel values in the region of interest, and Σ is the sum of (r 1 (x 1 i, y 1 i)-r 1 m) 2  in the region of interest. 
       σ1 2 ( x, y )=Σ( r 1( x 1 i, y 1 i )− r 1 m ) 2    (2)
 
     The structure-of-interest detection unit  33  detects a pixel having a variance value σ 1   2  equal to or greater than a predetermined threshold value Thl as a pixel of the calcification in the tomographic images Dj. In addition, the detection of the calcification is not limited to the method using the variance value. The pixel of the calcification may be detected by a filtering process using a filter that can extract a pixel having a brightness equal to or greater than a predetermined threshold value Th 2 . Further, the pixel of the calcification may be detected from the tomographic images Dj by CAD. 
     The selection unit  34  selects the high-frequency tomographic images DHj used to generate a composite two-dimensional image, which will be described below, for each corresponding pixel in each of the high-frequency tomographic images DHj on the basis of the feature amounts 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 the linear 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 linear structure images DSj are selected as the high-frequency tomographic images including the linear structure for each corresponding pixel in the high-frequency tomographic images DHj. Specifically, the selection unit  34  compares the feature amounts of the pixels corresponding to the pixel of interest in the linear structure images DSj for the corresponding pixel of interest in the high-frequency tomographic images DHj. Then, the selection unit  34  specifies a maximum of three linear structure images DSj having a large feature amount. 
     For example, in a case in which the feature amounts of the corresponding pixel of interest in the six linear structure images DS 1  to DS 6  are 10, 30, 10, 40, 20, and 50, respectively, the selection unit  34  specifies the linear structure images DS 2 , DS 4 , and DS 6  as the linear structure images having a large feature amount for the pixel of interest. Then, the selection unit  34  selects the high-frequency tomographic images DH 2 , DH 4 , and DH 6  corresponding to the specified linear structure images DS 2 , DS 4 , and DS 6  as the high-frequency tomographic images DHj including the linear structure for the pixel of interest. In addition, a maximum of three high-frequency tomographic images DHj having a feature amount equal to or greater than a predetermined threshold value Th 3  may be selected as the high-frequency tomographic images including the linear structure. 
     In addition, the number of selected high-frequency tomographic images DHj including the linear structure is not limited to a maximum of three. For example, for each corresponding pixel in the high-frequency tomographic images DHj, only one high-frequency tomographic image having the maximum feature amount in the linear structure images DSj may be selected as the high-frequency tomographic image including the linear structure. In addition, all of the high-frequency tomographic images having a feature amount equal to or greater than a predetermined threshold value Th 4  in the linear structure images DSj may be selected as the high-frequency tomographic images including the linear structure. Further, in a case in which the number of high-frequency tomographic images having a feature amount greater than 0 among the six high-frequency tomographic images DH 1  to DH 6  is less than three, the selection unit  34  may select one or two high-frequency tomographic images having a feature amount greater than 0 as the high-frequency tomographic images including the linear structure. 
     In addition, in a case in which all of the high-frequency tomographic images DHj have a feature amount of 0 in the linear structure images DSj in the corresponding pixel in the high-frequency tomographic images DHj, the selection unit  34  selects all of the high-frequency tomographic images DHj as the predetermined high-frequency tomographic images for the pixel. Further, for the corresponding pixels in the high-frequency tomographic images DHj, the high-frequency tomographic image DHj having the maximum pixel value (that is, the maximum brightness) may be selected as the predetermined high-frequency tomographic image. Furthermore, a predetermined number (for example, a maximum of three) of high-frequency tomographic images DHj in descending order of the pixel value in the corresponding pixel in the high-frequency tomographic images DHj may be selected as the predetermined high-frequency tomographic images. 
     Meanwhile, in the first embodiment, in a case in which the calcification is detected in the corresponding pixel in the high-frequency tomographic images DHj, the selection unit  34  selects the high-frequency tomographic image in which the calcification has been detected, regardless of the magnitude of the feature amount of the linear structure derived by the feature amount derivation unit  32 . For example, it is assumed that the calcification is detected in the pixel of interest in the high-frequency tomographic images DHj. In this case, even in a case in which the high-frequency tomographic image including the linear structure is selected for the pixel of interest, the high-frequency tomographic image in which the calcification has been detected is selected instead of the high-frequency tomographic image including the linear structure. In addition, in a case in which the predetermined high-frequency tomographic images are selected for the pixel of interest and the 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 has been detected, instead of the predetermined high-frequency tomographic images. 
     Further, in a case in which the number of high-frequency tomographic images in which the calcification has been detected in the pixel of interest is three or more, the selection unit  34  may select a maximum of three high-frequency tomographic images corresponding to the tomographic images in which the calcification has been detected, instead of the three high-frequency tomographic images including the linear structure. In this case, the selection unit  34  selects the top three high-frequency tomographic images having a large variance value at the time of detecting the calcification. 
     Furthermore, in a case in which the number of high-frequency tomographic images in which the calcification has been detected in the pixel of interest is equal to or less than two, the selection unit  34  may select two or one high-frequency tomographic image in which the calcification has been detected, instead of two or one of the high-frequency tomographic image including the linear structure. In this case, one or two high-frequency tomographic images including the linear structure may be left as they are. 
     For example, it is assumed that three high-frequency tomographic images DH 1 , DH 2 , and DH 3  including the linear structure are selected in the pixel of interest. It is assumed that the magnitudes of the feature amounts of the linear structures satisfy DH 1  &gt;DH 2  &gt;DH 3 . Further, it is assumed that the calcification is detected in the high-frequency tomographic image DH 4  for the pixel of interest. In this case, the selection unit  34  selects the high-frequency tomographic image DH 4  in the pixel of interest, instead of the high-frequency tomographic image DH 1  having the minimum feature amount among the high-frequency tomographic images DH 1 , DH 2 , and DH 3  including the linear structure. Further, it is assumed that the calcification is detected in the high-frequency tomographic images DH 4 , DHS, and DH 6  in the pixel of interest. In this case, the selection unit  34  selects the high-frequency tomographic images DH 4 , DHS, and DH 6  in the pixel of interest, instead of the high-frequency tomographic images DH 1 , DH 2 , and DH 3  including the linear structure. 
       FIG.  10    is a diagram illustrating the selection of the high-frequency tomographic image based on the feature amount and the calcification. In addition, in  FIG.  10   , the selection of the high-frequency tomographic images from the six high-frequency tomographic images DH 1  to DH 6  illustrated in  FIG.  8    will be described. Further, in  FIG.  10   , the high-frequency tomographic images DH 1  to DH 6  are schematically illustrated one-dimensionally. The high-frequency tomographic images DH 1  to DH 6  have 15 pixels P 1  to P 15 . Furthermore, in  FIG.  10   , reference numerals are given only to the pixels P 1 , P 5 , P 10 , and P 15  of the high-frequency tomographic image DH 6 . In addition, in  FIG.  10   , the feature amounts and the variance values of the calcifications derived from the linear structure images DS 1  to DS 6  are illustrated in each pixel of the high-frequency tomographic images DH 1  to DH 6 . 
     In  FIG.  10   , the feature amount of the linear structure is represented by a thick line, and the variance value of the calcification is represented by a thick white line. In addition, as the distance from the schematically illustrated high-frequency tomographic images DH 1  to DH 6  to the upper side becomes longer, the value of the feature amount of the linear structure becomes larger, and the variance value at the time of detecting the calcification becomes larger. In addition, here, in the following description, it is assumed that a maximum of three high-frequency tomographic images DHj including the linear structure having a feature amount equal to or greater than the threshold value are selected. Therefore,  FIG.  10    illustrates only the feature amounts of the linear structures which are equal to or greater than the threshold value. Furthermore, in the following description, the same figures as  FIG.  10    are illustrated in the same manner as  FIG.  10   . 
     In the pixel P 1 , the feature amount of the linear structure is derived in the high-frequency tomographic images DH 3  and DH 4 . In this case, the selection unit  34  selects the two high-frequency tomographic images DH 3  and DH 4  including the linear structure in the pixel P 1  . 
     In the pixel P 2 , the feature amount of the linear structure is derived in the high-frequency tomographic images DH 1  to DHS. Among the high-frequency tomographic images DH 1  to DHS, the high-frequency tomographic images having the top three feature amounts are the high-frequency tomographic images DH 2  to DH 4 . Therefore, the selection unit  34  selects the three high-frequency tomographic images DH 2  to DH 4  including the linear structure in the pixel P 2 . 
     In the pixel P 3 , the feature amount of the linear structure is derived in the high-frequency tomographic images DH 1  to DHS. Among the high-frequency tomographic images DH 1  to DHS, the high-frequency tomographic images having the top three feature amounts are the high-frequency tomographic images DH 1  to DH 3 . Therefore, the selection unit  34  selects the three high-frequency tomographic images DH 1  to DH 3  including the linear structure in the pixel P 3 . 
     In the pixel P 4 , the feature amount of the linear structure is derived in the high-frequency tomographic images DH 1  to DH 5 . Among the high-frequency tomographic images DH 1  to DH 5 , the high-frequency tomographic images having the top three feature amounts are the high-frequency tomographic images DH 2  to DH 4 . In addition, in the pixel P 4 , the calcification is detected in two high-frequency tomographic images DH 3  and DH 4 . Therefore, first, the selection unit  34  selects the two high-frequency tomographic images DH 3  and DH 4 , in which the calcification has been detected, in the pixel P 4 . In addition, the selection unit  34  selects one high-frequency tomographic image DH 2  including the linear structure excluding the high-frequency tomographic images DH 3  and DH 4 , which have already been selected, among the high-frequency tomographic images DH 2  to DH 4  having the top three feature amounts. Further, the selection unit  34  may select only the high-frequency tomographic images DH 3  and DH 4 , in which the calcification has been detected, in the pixel P 4 . 
     In the pixel P 5 , the feature amount of the linear structure is derived in three high-frequency tomographic images DH 2  to DH 4 . Therefore, the selection unit  34  selects the three high-frequency tomographic images DH 2  to DH 4  including the linear structure in the pixel P 5 . 
     In the pixel P 6 , the feature amount of the linear structure is not derived in any of the high-frequency tomographic images DH 1  to DH 6 . In addition, in the pixel P 6 , the calcification is detected in the high-frequency tomographic image DH 2 . Therefore, the selection unit  34  selects only the high-frequency tomographic image DH 2 , in which the calcification has been detected, in the pixel P 6 . 
     In the pixel P 7 , the feature amount of the linear structure is not derived in any of the high-frequency tomographic images DH 1  to DH 6 . In addition, the calcification is not detected. Therefore, the selection unit  34  selects all of the high-frequency tomographic images DH 1  to DH 6  as the predetermined high-frequency tomographic images in the pixel P 7 . 
     In the pixel P 8 , the feature amount of the linear structure is derived in three high-frequency tomographic images DH 1  to DH 3 . Therefore, the selection unit  34  selects the three high-frequency tomographic images DH 1  to DH 3  including the linear structure in the pixel P 8 . 
     In the pixel P 9 , the feature amount of the linear structure is derived in the high-frequency tomographic images DH 1  to DH 4 . Among the high-frequency tomographic images DH 1  to DH 4 , the high-frequency tomographic images having the top three feature amounts are the high-frequency tomographic images DH 1  to DH 3 . Meanwhile, in the pixel P 9 , the calcification is detected in three high-frequency tomographic images DH 4  to DH 6 . Therefore, the selection unit  34  selects the three high-frequency tomographic images DH 4  to DH 6 , in which the calcification has been detected, in the pixel P 9 . 
     In the pixel P 10 , the feature amount of the linear structure is derived in the high-frequency tomographic images DH 1  to DH 4 . Among the high-frequency tomographic images DH 1  to DH 4 , the high-frequency tomographic images having the top three feature amounts are the high-frequency tomographic images DH 1  to DH 3 . Therefore, the selection unit  34  selects the three high-frequency tomographic images DH 1  to DH 3  including the linear structure in the pixel P 10 . 
     In the pixels P 11  to P 13 , the feature amount of the linear structure is not derived in any of the high-frequency tomographic images DH 1  to DH 6 . In addition, the calcification is not detected. Therefore, the selection unit  34  selects all of the high-frequency tomographic images DH 1  to DH 6  as the predetermined high-frequency tomographic images in the pixels P 11  to P 13 . 
     In the pixel P 14 , the feature amount of the linear structure is derived in the high-frequency tomographic images DH 2  and DH 4 . Meanwhile, in the pixel P 14 , the calcification is detected in the three high-frequency tomographic images DH 3  and DH 5 . Therefore, the selection unit  34  selects the three high-frequency tomographic images DH 3  to DH 5 , in which the calcification has been detected, in the pixel P 14 . 
     In the pixel P 15 , the feature amount of the linear structure is not derived in any of the high-frequency tomographic images DH 1  to DH 6 . In addition, the calcification is not detected. Therefore, the selection unit  34  selects all of the high-frequency tomographic images DH 1  to DH 6  as the predetermined high-frequency tomographic images in the pixel P 15 . 
     The combination unit  35  derives a composite two-dimensional image on the basis of the high-frequency tomographic images selected by the selection unit  34 . That is, in a region of the linear structure and a region of the calcification, the combination unit  35  derives the composite two-dimensional image on the basis of the high-frequency tomographic images including the linear structure and the high-frequency tomographic images in which the calcification has been detected. In addition, in a region other than the linear structure and the calcification, a composite two-dimensional image is derived on the basis of the predetermined high-frequency tomographic images. 
     Here, in the first embodiment, the combination unit  35  derives a composite high-frequency image GH 1  which is a composite two-dimensional image for the high-frequency tomographic images DHj on the basis of the selected high-frequency tomographic images. In addition, the combination unit  35  derives a composite low-frequency image GL 1  which is a composite two-dimensional image for the low-frequency tomographic images DLj indicating low-frequency components of the tomographic images Dj used in a case in which the linear structure image derivation unit  31  derives the linear structure images DSj. Then, the combination unit  35  derives a composite two-dimensional image CG 1  from the composite high-frequency image GH 1  and the composite low-frequency image GL 1 . 
     First, the derivation of the composite high-frequency image GH 1  will be described. In addition, in the first embodiment, it is assumed that the high-frequency tomographic images are selected as described above with reference to  FIG.  10   . Therefore, combination for each of the pixels P 1  to P 15  will be described below. 
     In the pixel P 1 , two high-frequency tomographic images DH 3  and DH 4  including the linear structure are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 1  in the high-frequency tomographic images DH 3  and DH 4  according to the feature amounts (that is, the variance values) and sets the weighted average value as the pixel value of the pixel P 1  in the composite high-frequency image GH 1 . In addition, a weighting coefficient for the weighted average is derived such that it becomes larger as the feature amount becomes larger. In addition, an added average value may be used instead of the weighted average value. This holds for the following description. 
     In the pixel P 2 , three high-frequency tomographic images DH 2  to DH 4  including the linear structure are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 2  in the high-frequency tomographic images DH 2  to DH 4  according to the feature amounts and sets the weighted average value as the pixel value of the pixel P 2  in the composite high-frequency image GH 1 . 
     In the pixels P 3 , P 8 , and P 10 , three high-frequency tomographic images DH 1  to DH 3  including the linear structure are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 3 , P 8 , and P 10  in the high-frequency tomographic images DH 1  to DH 3  according to the feature amounts and sets the weighted average value as the pixel values of the pixels P 3 , P 8 , and P 10  in the composite high-frequency image GH 1 . 
     In the pixel P 4 , three high-frequency tomographic images DH 2  to DH 4  are selected. Among them, two high-frequency tomographic images DH 3  and DH 4  are selected on the basis of the detection result of the calcification. Therefore, first, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 4  in the high-frequency tomographic images DH 3  and DH 4  according to the magnitudes of the variance values at the time of detecting the calcification in the pixel P 4 . Then, the combination unit  35  derives an added average value of the weighted average value of the pixel values of the pixels P 4  in the high-frequency tomographic images DH 3  and DH 4  and the pixel value of the pixel P 4  in the high-frequency tomographic image DH 2  including the linear structure and sets the added average value as the pixel value of the pixel P 4  in the composite high-frequency image GH 1 . 
     In the pixel P 5 , three high-frequency tomographic images DH 2  to DH 4  including the linear structure are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 5  in the high-frequency tomographic images DH 2  to DH 4  according to the feature amounts and sets the weighted average value as the pixel value of the pixel P 5  in the composite high-frequency image GH 1 . 
     In the pixel P 6 , only the high-frequency tomographic image DH 2  in which the calcification has been detected is selected. Therefore, the combination unit  35  uses the pixel value of the pixel P 6  in the high-frequency tomographic image DH 2  as the pixel value of the pixel P 6  in the composite high-frequency image GH 1 . 
     In the pixels P 7 , P 11  to P 13 , and P 15 , all of the high-frequency tomographic images DH 1  to DH 6  are selected. Therefore, the combination unit  35  derives an added average value of the pixel values of the pixels P 7 , P 11  to P 13 , and P 15  in the high-frequency tomographic images DH 1  to DH 6  and sets the added average value as the pixel values of the pixels P 7 , P 11  to P 13 , and P 15  in the composite high-frequency image GH 1 . 
     In the pixel P 9 , three high-frequency tomographic images DH 4  to DH 6  in which the calcification has been detected are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 9  in the high-frequency tomographic images DH 4  to DH 6  according to the magnitudes of the variance values calculated at the time of detecting the calcification and sets the weighted average value as the pixel value of the pixel P 9  in the composite high-frequency image GH 1 . 
     In the pixel P 14 , three high-frequency tomographic images DH 3  to DH 5  in which the calcification has been detected are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 14  in the high-frequency tomographic images DH 3  to DH 5  according to the magnitudes of the variance values calculated at the time of detecting the calcification and sets the weighted average value as the pixel value of the pixel P 14  in the composite high-frequency image GH 1 . 
       FIG.  11    is a diagram illustrating the generation of a composite high-frequency image. In addition, the generation will be described using the six high-frequency tomographic images DH 1  to DH 6  in  FIG.  11   . As illustrated in  FIG.  11   , the high-frequency tomographic images DH 1  to DH 6  include linear structures and calcifications, and the linear structures and the calcifications included in the six high-frequency tomographic images DH 1  to DH 6  are combined and included in the composite high-frequency image GH 1 . 
     Here, the calcification K 11  included in the tomographic image D 1  is detected in the high-frequency tomographic image DH 1 . The calcification K 11  overlaps the linear structure K 21  included in the high-frequency tomographic image DH 2 . In this case, in the pixel corresponding to the calcification K 11 , the selection unit  34  selects the high-frequency tomographic image DH 1  in which the calcification K 11  has been detected, instead of the high-frequency tomographic image DH 2  including the linear structure. Therefore, in the composite high-frequency image GH 1 , the linear structure K 21  included in the high-frequency tomographic image DH 2  is overwritten with the calcification K 11  included in the high-frequency tomographic image DH 1 . 
     Further, the calcification K 61  included in the tomographic image D 6  is detected in the high-frequency tomographic image DH 6 . The calcification K 61  overlaps the linear structure K 51  included in the high-frequency tomographic image DH 5 . In this case, in the pixel corresponding to the calcification K 61 , the selection unit  34  selects the high-frequency tomographic image DH 6  in which the calcification K 61  has been detected, instead of the high-frequency tomographic image DH 5  including the linear structure. Therefore, in the composite high-frequency image GH 1 , the linear structure K 51  included in the high-frequency tomographic image DH 5  is overwritten with the calcification K 61  included in the high-frequency tomographic image DH 6 . 
     Meanwhile, the combination unit  35  derives an added average value of the pixel values of the corresponding pixels for all of the pixels of the low-frequency tomographic images DLj and sets the added average value as the pixel values of the composite low-frequency image GL 1 . In addition, the combination unit  35  may derive the composite low-frequency image GL 1  using any method such as a method that calculates a variance value of each pixel for each of the low-frequency tomographic images DLj and derives a weighted average value corresponding to the magnitude of the variance value. 
       FIG.  12    is a diagram illustrating the generation of a composite low-frequency image. In addition, in  FIG.  12   , the generation will be described using the six low-frequency tomographic images DL 1  to DL 6  illustrated in  FIG.  8   . As illustrated in  FIG.  12   , the low-frequency tomographic images DL 2  to DL 5  include low-frequency structures K 23 , K 33 , K 43 , and K 53 , such as tumors included in the breast M, respectively. Since the structures K 23 , K 33 , and K 43  respectively included in the low-frequency tomographic images DL 2  to DL 4  overlap each other, the composite low-frequency GL 1  includes a structure K 71  which is an overlap of the structures K 23 , K 33 , and K 43 . Since the structure K 53  included in the low-frequency tomographic image DL 5  does not overlap any of the structures included in the other low-frequency tomographic images DL 1  to DL 4  and DL 6 , the structure K 53  is included as it is in the composite low-frequency image GL 1 . 
     The combination unit  35  combines the composite high-frequency image GH 1  and the composite low-frequency image GL 1  to derive the composite two-dimensional image CG 1 . A method that corresponds to the derivation of the high-frequency linear structure by the linear structure image derivation unit  31  may be used as a combination method. For example, in a case in which the high-frequency tomographic images are derived by subtracting the enlarged low-frequency tomographic images DLj from the tomographic images Dj, the composite two-dimensional image CG 1  is derived using, for example, the method disclosed in JP2018-029746A. Specifically, the composite two-dimensional image CG 1  is derived by enlarging the low-frequency tomographic images DLj to have the same size as the original tomographic images Dj using an interpolation operation and adding the enlarged low-frequency tomographic images DLj and the composite high-frequency image GH 1 . In addition, the addition may be weighted addition. In this case, it is preferable that a weighting coefficient for the composite high-frequency image GH 1  is larger than that for the composite low-frequency image GL 1 . 
     The display control unit  36  displays the composite two-dimensional image CG 1  derived by the combination unit  35  on the display  24 .  FIG.  13    is a diagram illustrating a composite two-dimensional image display screen in the first embodiment. As illustrated in  FIG.  13   , the composite two-dimensional image CG 1  is displayed on a display screen  50  of the display  24 . In addition, the composite two-dimensional image CG 1  illustrated in  FIG.  13    is derived from the composite high-frequency image GH 1  illustrated in  FIG.  11    and the composite low-frequency image GL 1  illustrated in  FIG.  12   . Further, in  FIG.  13   , all of the structures included in the tomographic images illustrated in  FIG.  7    are not denoted by reference numerals. The composite two-dimensional image CG 1  illustrated in  FIG.  13    includes the linear structure, the calcification, and the low-frequency structure included in the tomographic images Dj. In particular, the calcification K 11  included in the tomographic image D 1  and the linear structure K 21  included in the tomographic image D 2  overlap each other. However, the linear structure K 21  is replaced with the calcification K 11  such that the calcification is easily seen. Further, the calcification K 61  included in the tomographic image D 6  and the linear structure K 51  included in the tomographic image D 5  overlap each other. However, the linear structure K 51  is replaced with the calcification K 61  such that the calcification is easily seen. 
     Next, a process performed in the first embodiment will be described.  FIG.  14    is a flowchart illustrating the process performed in the first embodiment. In addition, it is assumed that a plurality of tomographic images Dj are acquired in advance and stored in the storage  23 . The process is started in a case in which the input device  25  receives a process start instruction from an operator, and the linear structure image derivation unit  31  derives the linear structure images DSj from the plurality of tomographic images Dj (Step ST 1 ). Then, the feature amount derivation unit  32  derives the feature amount indicating the features of the linear structure from each of the plurality of linear structure images DSj (Step ST 2 ). In addition, the structure-of-interest detection unit  33  detects a calcification as the structure of interest from each of the plurality of tomographic images Dj or the high-frequency tomographic images DHj (Step ST 3 ). 
     Then, the selection unit  34  selects the high-frequency tomographic image for each corresponding pixel in each of the high-frequency tomographic images DHj indicating the high-frequency components of the tomographic images Dj (Step ST 4 ). That is, the selection unit  34  selects at least one high-frequency tomographic image including the linear structure or a predetermined high-frequency tomographic image for each corresponding pixel in each of the high-frequency tomographic images DHj on the basis of the feature amounts. In addition, in the corresponding pixel in the high-frequency tomographic images in which the calcification has been detected, the high-frequency tomographic images in which the calcification has been detected are selected instead of the high-frequency tomographic images including the linear structure or the predetermined high-frequency tomographic images. 
     Further, the combination unit  35  derives the composite two-dimensional image CG 1  on the basis of the selected high-frequency tomographic images (Step ST 5 ). Then, the display control unit  36  displays the composite two-dimensional image CG 1  on the display  24  (Step ST 6 ). Then, the process ends. 
     As described above, in the first embodiment, the linear structure images DSj are derived from the tomographic images Dj, and the feature amount indicating the features of the linear structure is derived from the linear structure images DSj. Then, at least one high-frequency tomographic image including the linear structure or a predetermined high-frequency tomographic image is selected for each corresponding pixel in each of the high-frequency tomographic images DHj on the basis of the feature amounts. Then, the composite two-dimensional image CG 1  is derived on the basis of the selected high-frequency tomographic images. Here, in a case in which the linear structures included in the breast M overlap in the depth direction of the breast M (that is, the direction in which the tomographic images Dj are arranged), the feature amount becomes large. Therefore, the high-frequency tomographic images including the linear structure are selected. Therefore, the linear structure is clearly included in the composite two-dimensional image CG 1 , without blurring. Therefore, according to this embodiment, it is possible to easily see a fine structure included in the breast M in the composite two-dimensional image CG 1 . 
     In addition, in the first embodiment, the calcification is detected as the structure of interest from the tomographic images Dj or the high-frequency tomographic images DHj. In the pixel in which the calcification has been detected, the high-frequency tomographic images in which the calcification has been detected are selected, instead of the high-frequency tomographic images DHj including the linear structure or the predetermined high-frequency tomographic images DHj. Here, the calcification is an important structure for diagnosing breast cancer. Therefore, even in a case in which the calcification included in the breast M overlaps the linear structure in the depth direction of the breast M, the high-frequency tomographic image including the calcification is selected. Therefore, the calcification is clearly included in the composite two-dimensional image CG 1  without being hidden by other structures in the breast M. Therefore, in the composite two-dimensional image CG 1 , the calcification included in the breast M can be easily seen. 
     In the first embodiment, the predetermined high-frequency tomographic images are selected in a case in which all of the feature amounts are 0 for each corresponding pixel in the high-frequency tomographic images DHj. However, the present disclosure is not limited thereto. In a case in which all of the feature amounts are less than a predetermined threshold value Th 5  for each corresponding pixel in the high-frequency tomographic images DHj, the predetermined high-frequency tomographic images may be selected. In this case, in a case in which there is a pixel having a feature amount equal to or greater than the threshold value Th 5 , the high-frequency tomographic images DHj including the pixel are selected as the high-frequency tomographic images including the linear structure. 
     Next, a second embodiment of the present disclosure will be described.  FIG.  15    is a diagram illustrating a functional configuration of an image processing device according to the second embodiment. In addition, in  FIG.  15   , the same components as those in  FIG.  4    are denoted by the same reference numerals, and the detailed description thereof will not be repeated. An image processing device  4 A according to the second embodiment differs from the image processing device  4  according to the first embodiment illustrated in  FIG.  4    in that it does not include the structure-of-interest detection unit  33 . In addition, in the second embodiment, the processes performed by the feature amount derivation unit  32 , the selection unit  34 , and the combination unit  35  are different from those in the first embodiment. 
     In the second embodiment, the feature amount derivation unit  32  converts the pixel value of each pixel of the linear structure images DSj to derive the feature amount. In the second embodiment, it is assumed that the pixel values of the linear structure images DSj have a larger value as brightness becomes higher (that is, closer to white). In addition, in the second embodiment, it is assumed that the average of the pixel values of the entire linear structure image is 0. That is, it is assumed that a pixel having high brightness (that is, white) has a positive value and a pixel having low brightness (that is, black) has a negative value. In the second embodiment, the pixel value of each pixel of the linear structure images DSj is converted, and an absolute value of the converted value is used as the feature amount. Specifically, the absolute value of a value obtained by adding a constant value to the pixel value of each pixel of the linear structure images DSj is used as the feature amount. Further, in a case in which the pixel value of each pixel of the linear structure images DSj is equal to or greater than 0, the absolute value of a value obtained by multiplying the pixel value of each pixel of the linear structure images DSj by al (a 1 &gt;1) or a value obtained by adding a constant value to the pixel value may be used as the feature amount. Furthermore, in a case in which the pixel value of each pixel of the linear structure images DSj is equal to or less than 0, the absolute value of a value obtained by multiplying the pixel value of each pixel of the linear structure images DSj by a 2  (a 2 &lt;1) or a value obtained by adding a constant value to the pixel value may be used as the feature amount. Therefore, as a linear structure likeness becomes higher, the feature amount related to the linear structure images DSj becomes larger. 
     In addition, in a case in which each pixel of the linear structure images DSj has a smaller pixel value as the brightness becomes higher and the pixel value of each pixel of the linear structure images DSj is equal to or less than 0, the feature amount may be derived by multiplying the pixel value of each pixel of the linear structure images DSj by a 3  (a 3 &gt;1) or by subtracting a constant value from the pixel value. Further, in a case in which the average of the pixel values of the entire image is not  0 , the feature amount may be calculated after the pixel values are converted such that the average of the pixel values is 0. 
     Here, in the first embodiment, the selection unit  34  selects the high-frequency tomographic images DHj. However, in the second embodiment, the selection unit  34  selects the tomographic images Dj used to generate the composite two-dimensional image on the basis of the feature amounts for each corresponding pixel in the tomographic images Dj, instead of the high-frequency tomographic images. In the second embodiment, at least one tomographic image including the linear structure or a predetermined tomographic image is selected for each corresponding pixel in each of the tomographic images Dj. 
     In addition, since the image processing device  4 A according to the second embodiment does not include the structure-of-interest detection unit  33 , the selection unit  34  according to the second embodiment selects the tomographic image without considering the calcification. That is, among the tomographic images Dj, a maximum of three tomographic images having a large feature amount in the linear structure images DSj are selected as the tomographic images including the linear structure for each corresponding pixel in the tomographic images Dj. Specifically, the selection unit  34  compares the feature amounts of the pixels corresponding to the pixels of interest in the linear structure images DSj for the corresponding pixel of interest in the tomographic images Dj. Then, the selection unit  34  specifies a maximum of three linear structure images DSj having a large feature amount. 
     For example, in a case in which the feature amounts of the corresponding pixel of interest in the six linear structure images DS 1  to DS 6  are 10, 30, 10, 40, 20, and 50, respectively, the selection unit  34  specifies the linear structure images DS 2 , DS 4 , and DS 6  as the maximum of three linear structure images having a large feature amount for the pixel of interest. Then, the selection unit  34  selects the tomographic images D 2 , D 4 , and D 6  corresponding to the specified linear structure images as the tomographic images including the linear structure for the pixel of interest. In addition, a maximum of three tomographic images having a feature amount equal to or greater than a predetermined threshold value Th 6  may be selected as the tomographic images including the linear structure. 
     In addition, the number of selected tomographic images Dj including the linear structure is not limited to a maximum of three. For example, for each corresponding pixel in the tomographic images Dj, only one tomographic image having the maximum feature amount in the linear structure images DSj may be selected as the tomographic image including the linear structure. In addition, all of the tomographic images having a feature amount equal to or greater than a predetermined threshold value Th 7  in the linear structure images DSj may be selected as the tomographic images including the linear structure. Further, in a case in which the number of tomographic images having a feature amount greater than 0 is less than three among the six tomographic images D 1  to D 6 , the selection unit  34  may select one or two tomographic images having a feature amount greater than 0 as the tomographic images including the linear structure. 
     Furthermore, in a case in which all of the tomographic images Dj have a feature amount of 0 in the linear structure images DSj for each corresponding pixel in the tomographic images Dj, the selection unit  34  selects all of the tomographic images Dj as the predetermined tomographic images. 
       FIG.  16    is a diagram illustrating the selection of the tomographic image using the feature amount in the second embodiment. In addition, in  FIG.  16   , the selection of the tomographic image from the six tomographic images D 1  to D 6  illustrated in  FIG.  7    will be described. Further, in  FIG.  16   , the tomographic images D 1  to D 6  are schematically illustrated one-dimensionally. The illustration in  FIG.  16    is the same as that in  FIG.  10    except that the tomographic images D 1  to D 6  are used. 
     In the pixel P 1 , the feature amount of the linear structure is derived in the tomographic images D 3  and D 4 . In this case, the selection unit  34  selects the two tomographic images D 3  and D 4  including the linear structure in the pixel P 1 . 
     In the pixel P 2 , the feature amount of the linear structure is derived in the tomographic images D 1  to D 5 . Among the tomographic images D 1  to D 5 , the tomographic images having the top three feature amounts are the tomographic images D 2  to D 4 . Therefore, the selection unit  34  selects the three tomographic images D 2  to D 4  including the linear structure in the pixel P 2 . 
     In the pixel P 3 , the feature amount of the linear structure is derived in the tomographic images D 1  to D 5 . Among the tomographic images D 1  to D 5 , the tomographic images having the top three feature amounts are the tomographic images D 1  to D 3 . Therefore, the selection unit  34  selects the three tomographic images D 1  to D 3  including the linear structure in the pixel P 3 . 
     In the pixel P 4 , the feature amount of the linear structure is derived in the tomographic images D 1  to D 5 . Among the tomographic images D 1  to D 5 , the tomographic images having the top three feature amounts are the tomographic images D 2  to D 4 . Therefore, the selection unit  34  selects the three tomographic images D 2  to D 4  including the linear structure in the pixel P 4 . 
     Since the feature amount of the linear structure is derived in three tomographic images D 2  to D 4  in the pixel P 5 , the selection unit  34  selects the three tomographic images D 2  to D 4  including the linear structure in the pixel P 5 . 
     In the pixels P 6  and P 7 , the feature amount of the linear structure is not derived in any of the tomographic images D 1  to D 6 . Therefore, the selection unit  34  selects all of the tomographic images D 1  to D 6  as the predetermined tomographic images in the pixels P 6  and P 7 . 
     In the pixel P 8 , the feature amount of the linear structure is derived from three tomographic images D 1  to D 3 . Therefore, the selection unit  34  selects the three tomographic images D 1  to D 3  including the linear structure in the pixel P 8 . 
     In the pixels P 9  and P 10 , the feature amount of the linear structure is derived in the tomographic images D 1  to D 4 . Among the tomographic images D 1  to D 4 , the tomographic images having the top three feature amounts are the tomographic images D 1  to D 3  in any of the pixels P 9  and P 10 . Therefore, the selection unit  34  selects the three tomographic images D 1  to D 3  including the linear structure in the pixels P 9  and P 10 . 
     In the pixels P 11  to P 13  and P 15 , the feature amount of the linear structure is not derived in any of the tomographic images D 1  to D 6 . Therefore, the selection unit  34  selects all of the tomographic images D 1  to D 6  as the predetermined tomographic images in the pixels P 11  to P 13  and P 15 . 
     In the pixel P 14 , the feature amount of the linear structure is derived in the tomographic images D 2  and D 3 . Therefore, the selection unit  34  selects the two tomographic images D 2  and D 3  including the linear structure in the pixel P 14 . 
     In the second embodiment, the combination unit  35  derives a composite two-dimensional image CG 2  on the basis of the selected tomographic images. 
     In the pixel P 1 , the tomographic images D 3  and D 4  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 1  in the tomographic images D 3  and D 4  according to the feature amounts derived for the corresponding linear structure images DS 3  and DS 4  and sets the weighted average value as the pixel value of the pixel P 1  in the composite two-dimensional image CG 2 . In addition, a weighting coefficient for the weighted average is derived such that it becomes larger as the feature amount becomes larger. In addition, an added average value may be used instead of the weighted average value. This holds for the following description. 
     In the pixels P 2 , P 4 , and P 5 , the tomographic images D 2  to D 4  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 2 , P 4 , and P 5  in the tomographic images D 2  to D 4  according to the feature amounts and sets the weighted average value as the pixel values of the pixels P 2 , P 4 , and P 5  in the composite two-dimensional image CG 2 . 
     In the pixels P 3  and P 8  to P 10 , the tomographic images D 1  to D 3  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 3  and P 8  to P 10  in the tomographic images D 1  to D 3  according to the feature amounts and sets the weighted average value as the pixel values of the pixels P 3  and P 8  to P 10  in the composite two-dimensional image CG 2 . 
     In the pixels P 6 , P 7 , P 11  to P 13 , and P 15 , all of the tomographic images D 1  to D 6  are selected. Therefore, the combination unit  35  derives an added average value of the pixel values of the pixels P 6 , P 7 , P 11  to P 13 , and P 15  in the tomographic images D 1  to D 6  and sets the added average value as the pixel values of the pixels P 6 , P 7 , P 11  to P 13 , and P 15  in the composite two-dimensional image CG 2 . 
     In the pixel P 14 , two tomographic images D 2  and D 3  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 14  in the tomographic images D 2  and D 3  according to the feature amounts and sets the weighted average value as the pixel value of the pixel P 14  in the composite two-dimensional image CG 2 . 
     The display control unit  36  displays the composite two-dimensional image CG 2  generated by the combination unit  35  on the display  24 .  FIG.  17    is a diagram illustrating a composite two-dimensional image display screen in the second embodiment. The composite two-dimensional image CG 2  is displayed on a display screen  50  of the display  24  as illustrated in  FIG.  17   . In addition, the composite two-dimensional image CG 2  illustrated in  FIG.  17    is generated from the tomographic images Dj illustrated in  FIG.  7   . In addition, in  FIG.  17   , all of the structures included in the tomographic images illustrated in  FIG.  7    are not denoted by reference numerals. The composite two-dimensional image CG 2  illustrated in  FIG.  17    includes the linear structure, the calcification, and the low-frequency structure included in the tomographic images Dj. 
     Here, in a case in which the composite two-dimensional image CG 2  illustrated in  FIG.  17    is compared with the composite two-dimensional image CG 1  according to the first embodiment illustrated in  FIG.  13   , the calcification K 11  included in the tomographic image D 1  and the linear structure K 21  included in the tomographic image D 2  overlap each other in the composite two-dimensional image CG 2 . Further, the calcification K 61  included in the tomographic image D 6  and the linear structure K 51  included in the tomographic image D 5  overlap each other. 
     Next, a process performed in the second embodiment will be described.  FIG.  18    is a flowchart illustrating the process performed in the second embodiment. In addition, it is assumed that a plurality of tomographic images Dj are acquired in advance and stored in the storage  23 . The process is started in a case in which the input device  25  receives a process start instruction from the operator, and the linear structure image derivation unit  31  derives the linear structure images DSj from the plurality of tomographic images Dj (Step ST 11 ). Then, the feature amount derivation unit  32  derives the feature amount indicating the features of the linear structure from each of the plurality of linear structure images DSj (Step ST 12 ). 
     Then, the selection unit  34  selects the tomographic image on the basis of the feature amount for each corresponding pixel in each of the tomographic images Dj (Step ST 13 ). That is, the selection unit  34  selects at least one tomographic image including the linear structure or a predetermined tomographic image for each corresponding pixel in each of the tomographic images Dj on the basis of the feature amount. 
     Further, the combination unit  35  derives the composite two-dimensional image CG 2  on the basis of the selected tomographic images (Step ST 14 ). Then, the display control unit  36  displays the composite two-dimensional image CG 2  on the display  24  (Step ST 15 ). Then, the process ends. 
     In addition, the image processing device  4 A according to the second embodiment may be provided with the structure-of-interest detection unit  33  as in the first embodiment. In this case, the selection unit  34  selects the tomographic images Dj used to generate the composite two-dimensional image CG 2  on the basis of the feature amounts derived by the feature amount derivation unit  32  and the calcification detected by the structure-of-interest detection unit  33  for each corresponding pixel in the tomographic images Dj. 
     Further, in the second embodiment, the selection unit  34  selects the tomographic images Dj, and the combination unit  35  combines the tomographic images Dj to derive the composite two-dimensional image CG 2 . However, the present disclosure is not limited thereto. As in the first embodiment, the selection unit  34  may select the high-frequency tomographic images DHj used to generate the composite two-dimensional image on the basis of the feature amounts derived by the feature amount derivation unit  32 . In this case, as in the first embodiment, the combination unit  35  may derive a composite high-frequency image (represented by GH 2 ) and a composite low-frequency image (represented by GL 2 ) and combine the composite high-frequency image GH 2  and the composite low-frequency image GL 2  to derive the composite two-dimensional image CG 2 . 
     Next, a third embodiment of the present disclosure will be described. In addition, a functional configuration of an 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 except only the process to be performed. Therefore, the detailed description of the device will not be repeated here. The third embodiment differs from the first embodiment in the processes performed by the linear structure image derivation unit  31 , the feature amount derivation unit  32 , the selection unit  34 , and the combination unit  35 . 
     In the third embodiment, the linear structure image derivation unit  31  performs a filtering process using a high-pass filter on the tomographic images Dj to derive the high-frequency tomographic images DHj indicating the high-frequency components of the tomographic images Dj. In addition, the linear structure image derivation unit  31  performs a filtering process using a low-pass filter on the tomographic images Dj to derive the low-frequency tomographic images DLj indicating the low-frequency components of the tomographic images Dj. Then, the linear structure image derivation unit  31  highlights the high-frequency components of the tomographic images Dj on the basis of the high-frequency tomographic images DHj. Specifically, the high-frequency components of the tomographic images Dj are highlighted by multiplying the pixel values of the pixels of the tomographic images Dj, which correspond to the pixels having pixel values equal to or greater than a threshold value in the high-frequency tomographic images DHj, by a 4  (a 4 &gt;1) or by adding a constant value to the pixel values. In addition, the linear structure image derivation unit  31  suppresses the low-frequency components of the tomographic images Dj, in which the high-frequency components have been highlighted, on the basis of the low-frequency tomographic images DLj. Specifically, the low-frequency components of the tomographic images Dj, in which the high-frequency components have been highlighted, are suppressed by multiplying the pixel values of the pixels of the tomographic images Dj, which correspond to the pixels having pixel values equal to or greater than a threshold value in the low-frequency tomographic images DLj, by a 5  (a 5 &lt;1) or by subtracting a constant value from the pixel values. 
     Further, the linear structure image derivation unit  31  applies a directional filter to the tomographic images Dj in the direction in which the pixels of the tomographic images Dj, in which the high-frequency components have been highlighted and the low-frequency components have been suppressed, are connected to extract the high-frequency linear structures, thereby deriving the linear structure images DSj. In the third embodiment, in a case in which the linear structure images DSj are derived, only the highlighting of the high-frequency components of the tomographic images Dj may be performed. In this case, the derivation of the low-frequency tomographic images DLj is unnecessary. In addition, in the third embodiment, in a case in which the linear structure images DSj are derived, only the suppression of the low-frequency components of the tomographic images Dj may be performed. In this case, the derivation of the high-frequency tomographic images DHj is unnecessary. 
     In the third embodiment, the selection unit  34  selects the tomographic images Dj used to generate the composite two-dimensional image on the basis of the feature amounts derived by the feature amount derivation unit  32  and the calcification detected by the structure-of-interest detection unit  33  for each corresponding pixel in the tomographic images Dj. In this case, first, the selection unit  34  selects the tomographic images Dj on the basis of the calcification detected by the structure-of-interest detection unit  33 . That is, in a case in which the calcification is detected in the corresponding pixels in the tomographic images Dj, in the third embodiment, first, the selection unit  34  selects the tomographic images Dj in which the calcification has been detected. In this case, as in the first embodiment, a predetermined number (for example, a maximum of 3) of tomographic images Dj, in which a variance value at the time of detecting the calcification is equal to or greater than a threshold value Th 8 , are selected. In addition, all of the tomographic images Dj, in which the calcification has been detected, may be selected. Further, one tomographic image Dj having the maximum variance value at the time of detecting the calcification may be selected. 
     For example, in a case in which the calcification is detected in the tomographic images D 2  and D 3  for the corresponding pixels of interest in the six tomographic images D 1  to D 6 , the selection unit  34  selects the tomographic images D 2  and D 3  as the tomographic images used to generate the composite two-dimensional image for the pixel of interest. In addition, in a case in which the calcification is detected in the tomographic images, whose number is greater than a predetermined number, for the pixel of interest, the tomographic images having a predetermined number of high-ranking variance values at the time of detecting the calcification may be selected. 
     Further, in the third embodiment, for the pixels in which the calcification has not been detected, the selection unit  34  selects at least one tomographic image including the linear structure or a predetermined tomographic image on the basis of the feature amount for each corresponding pixel in the tomographic images Dj. The selection of the at least one tomographic image including the linear structure or the predetermined tomographic image is performed in the same manner as that in the second embodiment. 
       FIG.  19    is a diagram illustrating the selection of the tomographic image in the third embodiment. In addition, in  FIG.  19   , the selection of the tomographic image from the six tomographic images D 1  to D 6  illustrated in  FIG.  7    will be described. Further, in  FIG.  19   , the tomographic images D 1  to D 6  are schematically illustrated one-dimensionally. Furthermore, the illustration in  FIG.  19    is the same as that in  FIG.  10    except that the tomographic images D 1  to D 6  are used. 
     The calcification is detected in the pixels P 4 , P 6 , P 9 , and P 14  as illustrated in  FIG.  19   . In the pixel P 4 , the calcification is detected in the tomographic images D 3  and D 4 . Therefore, the selection unit  34  selects the tomographic images D 3  and D 4  in the pixel P 4 . In the pixel P 6 , the calcification is detected in the tomographic image D 2 . Therefore, the selection unit  34  selects the tomographic image D 2  in the pixel P 6 . In the pixel P 9 , the calcification is detected in the tomographic images D 4  to D 6 . Therefore, the selection unit  34  selects the tomographic images D 4  to D 6  in the pixel P 9 . In the pixel P 14 , the calcification is detected in the tomographic images D 3  to D 5 . Therefore, the selection unit  34  selects the tomographic images D 3  to D 5  in the pixel P 14 . 
     Further, in the pixels P 1  to P 3 , P 5 , P 7 , P 8 , P 10  to P 13 , and P 15  in which the calcification has not been detected, the selection unit  34  selects the tomographic images Dj on the basis of the feature amounts derived by the feature amount derivation unit  32  for each corresponding pixel in the tomographic images Dj. 
     Next, the pixels P 1  to P 3 , P 5 , P 7 , P 8 , P 10  to P 13 , and P 15  in which the calcification has not been detected will be described. 
     In the pixel P 1 , the feature amount of the linear structure is derived in the tomographic images D 3  and D 4 . In this case, the selection unit  34  selects the two tomographic images D 3  and D 4  including the linear structure in the pixel P 1 . 
     In the pixel P 2 , the feature amount of the linear structure is derived in the tomographic images D 1  to D 5 . Among the tomographic images D 1  to D 5 , the tomographic images having the top three feature amounts are the tomographic images D 2  to D 4 . Therefore, the selection unit  34  selects the three tomographic images D 2  to D 4  including the linear structure in the pixel P 2 . 
     In the pixel P 3 , the feature amount of the linear structure is derived in the tomographic images D 1  to D 5 . Among the tomographic images D 1  to D 5 , the tomographic images having the top three feature amounts are the tomographic images D 1  to D 3 . Therefore, the selection unit  34  selects the three tomographic images D 1  to D 3  including the linear structure in the pixel P 3 . 
     In the pixel P 5 , the feature amount of the linear structure is derived in three tomographic images D 2  to D 4 . Therefore, the selection unit  34  selects the three tomographic images D 2  to D 4  including the linear structure in the pixel P 5 . 
     In the pixel P 7 , the feature amount of the linear structure is not derived in any of the tomographic images D 1  to D 6 . Therefore, the selection unit  34  selects all of the tomographic images D 1  to D 6  as the predetermined tomographic images in the pixel P 7 . 
     In the pixel P 8 , the feature amount of the linear structure is derived from three tomographic images D 1  to D 3 . Therefore, the selection unit  34  selects the three tomographic images D 1  to D 3  including the linear structure in the pixel P 8 . 
     In the pixel P 10 , the feature amount of the linear structure is derived in the tomographic images D 1  to D 4 . Among the tomographic images D 1  to D 4 , the tomographic images having the top three feature amounts are the tomographic images D 1  to D 3 . Therefore, the selection unit  34  selects the three tomographic images D 1  to D 3  including the linear structure in the pixel P 10 . 
     In the pixels P 11  to P 13  and P 15 , the feature amount of the linear structure is not derived in any of the tomographic images D 1  to D 6 . Therefore, the selection unit  34  selects all of the tomographic images D 1  to D 6  as the predetermined tomographic images in the pixels P 11  to P 13  and P 15 . 
     In the third embodiment, the combination unit  35  derives a composite two-dimensional image CG 3  on the basis of the selected tomographic images in a region of the linear structure and the calcification and derives the composite two-dimensional image CG 3  on the basis of the predetermined tomographic images in a region other than the linear structure and the calcification. 
     In the pixel P 1 , the tomographic images D 3  and D 4  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 1  in the tomographic images D 3  and D 4  according to the feature amounts derived for the corresponding linear structure images DS 3  and DS 4  and sets the weighted average value as the pixel value of the pixel P 1  in the composite two-dimensional image CG 3 . In addition, a weighting coefficient for the weighted average is derived such that it becomes larger as the feature amount becomes larger. In addition, an added average value may be used instead of the weighted average value. This holds for the following description. 
     In the pixels P 2  and P 5 , the tomographic images D 2  to D 4  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 2  and P 5  in the tomographic images D 2  to D 4  according to the feature amounts and sets the weighted average value as the pixel values of the pixels P 2  and P 5  in the composite two-dimensional image CG 3 . 
     In the pixels P 3 , P 8 , and P 10 , the tomographic images D 1  to D 3  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 3 , P 8 , and P 10  in the tomographic images D 1  to D 3  according to the feature amounts and sets the weighted average value as the pixel values of the pixels P 3 , P 8 , and P 10  in the composite two-dimensional image CG 3 . 
     In the pixel P 4 , the tomographic images D 3  and D 4  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 4  in the tomographic images D 3  and D 4  according to the variance value used at the time of detecting the calcification and sets the weighted average values as the pixel value of the pixel P 4  in the composite two-dimensional image CG 3 . 
     In the pixel P 6 , the tomographic image D 2  is selected. Therefore, the combination unit  35  sets the pixel value of the pixel P 6  in the tomographic image D 2  as the pixel value of the pixel P 6  in the composite two-dimensional image CG 3 . 
     In the pixels P 7 , P 11  to P 13 , and P 15 , all of the tomographic images D 1  to D 6  are selected. Therefore, the combination unit  35  derives an added average value of the pixel values of the pixel values of the pixels P 7 , P 11  to P 13 , and P 15  in the tomographic images D 1  to D 6  and sets the added average value as the pixel values of the pixels P 7 , P 11  to P 13 , and P 15  in the composite two-dimensional image CG 3 . 
     In the pixel P 9 , the tomographic images D 4  to D 6  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 9  in the tomographic images D 4  to D 6  according to the variance values used at the time of detecting the calcification and sets the weighted average value as the pixel value of the pixel P 9  in the composite two-dimensional image CG 3 . 
     In the pixel P 14 , the tomographic images D 3  to D 5  are selected. Therefore, the combination unit  35  derives a weighted average value of the pixel values of the pixels P 14  in the tomographic images D 3  to D 6  according to the variance value used at the time of detecting the calcification and sets the weighted average value as the pixel value of the pixel P 14  in the composite two-dimensional image CG 3 . 
     In addition, the composite two-dimensional image CG 3  derived in the third embodiment is substantially the same as the composite two-dimensional image CG 1  derived in the first embodiment. 
     Next, a process performed in the third embodiment will be described.  FIG.  20    is a flowchart illustrating the process performed in the third embodiment. In addition, it is assumed that a plurality of tomographic images Dj are acquired in advance and stored in the storage  23 . The process is started in a case in which the input device  25  receives a process start instruction from the operator, and the linear structure image derivation unit  31  derives the linear structure images DSj from a plurality of tomographic images Dj (Step ST 21 ). Then, the feature amount derivation unit  32  derives the feature amount indicating the features of the linear structure from each of the plurality of linear structure images DSj (Step ST 22 ). In addition, the structure-of-interest detection unit  33  detects the calcification as the structure of interest from each of the plurality of tomographic images Dj (Step ST 23 ). 
     Then, the selection unit  34  selects at least one tomographic image including the calcification on the basis of the detected calcification for each corresponding pixel in each of the tomographic images Dj (Step ST 24 ). In addition, the selection unit  34  selects the tomographic image on the basis of the feature amount for each corresponding pixel in each of the tomographic images Dj (Step ST 25 ). That is, the selection unit  34  selects at least one tomographic image including the linear structure or a predetermined tomographic image for each corresponding pixel in each of the tomographic images Dj on the basis of the feature amount. 
     Further, the combination unit  35  derives the composite two-dimensional image CG 3  on the basis of the selected tomographic images (Step ST 26 ). Then, the display control unit  36  displays the composite two-dimensional image CG 3  on the display  24  (Step ST 27 ). Then, the process ends. 
     In addition, in the first embodiment, the structure-of-interest detection unit  33  detects the calcification as the structure of interest from the tomographic images Dj, and the tomographic image is selected also on the basis of the calcification. However, the present disclosure is not limited thereto. In the first embodiment, the structure-of-interest detection unit  33  may not be provided, and the high-frequency tomographic images DHj may be selected on the basis of only the feature amounts. 
     In addition, in the first embodiment, the feature amount derivation unit  32  may derive the feature amount as in the second or third embodiment. 
     In addition, in the first and second embodiments, the linear structure image derivation unit  31  may derive the linear structure image as in the third embodiment. 
     Further, in the first embodiment, the composite high-frequency image may be derived in advance by calculating, for example, the weighted average value of the pixel values of the corresponding pixels in the high-frequency tomographic images DHj. The composite high-frequency image derived in advance is referred to as a pre-composite high-frequency image. In this case, the selection unit  34  selects only the high-frequency tomographic image including the linear structure or the high-frequency tomographic image in which the calcification has been detected. Further, in this case, the combination unit  35  derives the composite high-frequency image GH 1  using the pre-composite high-frequency image. 
     Specifically, among the pixels of the high-frequency tomographic images DHj, for the pixel for which the high-frequency tomographic image including the linear structure has been selected and the pixel for which the high-frequency tomographic image in which the calcification has been detected has been selected, the pixel values of the composite high-frequency image GH 1  are derived using the high-frequency tomographic image including the linear structure and the high-frequency tomographic image in which the calcification has been detected. On the other hand, for the pixel for which the high-frequency tomographic image including the linear structure or the high-frequency tomographic image in which the calcification has been detected is not selected, the pixel value of the corresponding pixel in the pre-composite high-frequency image is used as the pixel value of the composite high-frequency image GH 1 . In addition, the pixel values of the composite high-frequency image GH 1  may be derived using the high-frequency tomographic image including the linear structure and the high-frequency tomographic image in which the calcification has been detected, and the derived pixel values may be added to the pre-composite high-frequency image to derive the composite high-frequency image GH 1 . 
     Further, in the second and third embodiments, the composite two-dimensional image may be derived in advance by calculating, for example, a weighted average value of the pixel values of the corresponding pixels in the tomographic images Dj. The composite two-dimensional image derived in advance is referred to as a pre-composite image. In this case, the selection unit  34  selects only the tomographic image including the linear structure or the tomographic image in which the calcification has been detected. Further, in this case, the combination unit  35  derives the composite two-dimensional images CG 2  and CG 3  using the pre-composite image. 
     Specifically, among the pixels of the tomographic images Dj, for the pixels for which the tomographic image including the linear structure and the tomographic image in which the calcification has been detected are selected, the pixel values of the composite two-dimensional images CG 2  and CG 3  are derived using the tomographic images including the linear structure and the tomographic images in which the calcification has been detected. On the other hand, for the pixel for which the tomographic image including the linear structure or the tomographic image in which the calcification has been detected is not selected, the pixel value of the corresponding pixel in the pre-composite image is used as the pixel value of the composite two-dimensional images CG 2  and CG 3 . In addition, the pixel values of the composite two-dimensional images CG 2  and CG 3  may be derived using the tomographic images including the linear structure and the tomographic images in which the calcification has been detected, and the derived pixel values may be added to the pre-composite image to derive the composite two-dimensional images CG 2  and CG 3 . 
     Further, the radiation in each of the above-described embodiments is not particularly limited. For example, a-rays or y-rays can be applied in addition to the X-rays. 
     Furthermore, in each of the above-described embodiments, for example, the following various processors can be used as a hardware structure of processing units performing various processes, such as the image acquisition unit  30 , the linear structure image derivation unit  31 , the feature amount derivation unit  32 , the structure-of-interest detection unit  33 , the selection unit  34 , the combination unit  35 , and the display control unit  36 . The various processors include, for example, a CPU which is a general-purpose processor executing software (program) to function as various processing units as described above, a programmable logic device (PLD), such as a field programmable gate array (FPGA), which is a processor whose circuit configuration can be changed after manufacture, and a dedicated electric circuit, such as an application specific integrated circuit (ASIC), which is a processor having a dedicated circuit configuration designed to perform a specific process. 
     One processing unit may be configured by one of the various processors or a combination of two or more processors of the same type or different types (for example, a combination of a plurality of FPGAs or a combination of a CPU and an FPGA). In addition, a plurality of processing units may be configured by one processor. 
     A first example of the configuration in which a plurality of processing units are configured by one processor is an aspect in which one processor is configured by a combination of one or more CPUs and software and functions as a plurality of processing units. A representative example of this aspect is a client computer or a server computer. A second example of the configuration is an aspect in which a processor that implements the functions of the entire system including a plurality of processing units using one integrated circuit (IC) chip is used. A representative example of this aspect is a system-on-chip (SoC). As such, various processing units are configured by using one or more of the various processors as a hardware structure. 
     In addition, specifically, an electric circuit (circuitry) obtained by combining circuit elements, such as semiconductor elements, can be used as the hardware structure of the various processors.