Patent Publication Number: US-10319093-B2

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

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to an image processing apparatus, which is configured to process a medical image, an image processing method, and a program. 
     Description of the Related Art 
     In recent years, image quality of medical images taken by three-dimensional imaging device, such as an X-ray computed tomography apparatus (X-ray CT) and a magnetic resonance imaging device (MRI), has dramatically improved, and detailed information on the inside of a human body can now be obtained. However, the number of images has increased with the improvement in image quality, and a burden of reading the images on radiologists has increased. As a result, there is an increasing expectation for computer aided diagnosis (CAD). 
     It is particularly important to automatically detect an abnormal shadow region, which is highly likely to be an abnormal tissue, such as a tumor, or automatically distinguish benignancy and malignancy of an anatomical tissue. For example, in processing for automatically detecting an abnormal shadow region and processing for distinguishing benignancy and malignancy of a tissue by CAD, there is a need to calculate a shape feature amount representing a shape of a contour (boundary) of a site of a living body, which is an object, to thereby automatically calculate a likelihood of an abnormal shadow or automatically calculate malignancy of the tissue. 
     In the processing for distinguishing benignancy and malignancy of a pulmonary nodule, there have been reported that a lobulated contour (boundary) of the pulmonary nodule is one indicator of malignancy, and that a polygonal or polyhedral contour of the pulmonary nodule is one indicator of benignancy. 
     However, such an abnormal tissue is often included as a shadow (hereinafter referred to as “attached abnormal shadow”) of a tissue (or site) in a state of being adhere to an organ or blood vessels in a medical image. Further, a surface (hereinafter referred to as “attached surface”) that is adhere to the organ or blood vessels and a surface (hereinafter referred to as “non-attached surface”) that is not adhere to the organ or blood vessels often have different shape feature amounts. A shadow of a tissue (or site) in a state of being not adhere to an organ or blood vessels is referred to as a “non-attached abnormal shadow”. 
     Clinically, a medical doctor observes a contour (boundary) of a non-attached surface of an attached abnormal shadow to diagnose a tissue. Therefore, in calculating a shape feature amount of a region of the attached abnormal shadow by CAD, when the shape feature amount is calculated including a contour (boundary) of an attached surface, the calculated shape feature amount may differ from a shape feature amount of the contour (boundary) of the non-attached surface that the doctor wants to observe. 
     To address the above-mentioned problem, in Japanese Patent Application Laid-Open No. 2009-225990, there is disclosed a technology for calculating a circularity using a non-attached surface. Specifically, there is disclosed a technology of obtaining a plurality of nodes forming a polygonal line that approximates a contour of a nodular region, determining a position of a reference point, and calculating a circularity using areas of a plurality of regions determined based on the plurality of nodes and the reference point. 
     SUMMARY OF THE INVENTION 
     However, there are many shape feature amounts that need to be calculated from a closed curved surface (line), such as a feature amount of spherical harmonics (SPHARM) (for example, shape vector) and a feature amount based on a major diameter and a minor diameter of an approximate ellipsoid. In the technology disclosed in Japanese Patent Application Laid-Open No. 2009-225990, the circularity is calculated using the non-attached surface excluding the attached surface, and hence such feature amounts cannot be obtained. Further, in Japanese Patent Application Laid-Open No. 2009-225990, a center of gravity of a pulmonary nodule, which is to be used in the calculation of the circularity, is determined in a method similar to that for the region of the non-attached abnormal shadow, and hence there is a possibility that the center of gravity of the region of the attached abnormal shadow cannot be determined accurately. 
     It is an object of the present invention to provide an image processing apparatus, an image processing method, and a program, the image processing apparatus being capable of appropriately calculating a shape feature amount, which is to be used in computer aided diagnosis (including a technology for automatically detecting an abnormal shadow region and a technology for distinguishing benignancy and malignancy of a tissue), for a site included as an attached abnormal shadow in a medical image. 
     According to one embodiment of the present invention, there is provided an image processing apparatus, including: a boundary extraction unit configured to extract a boundary (entire boundary) of a first site and a boundary (entire boundary) of a second site in a medical image; a boundary identification unit configured to identify a partial boundary of the first site that is adhere to the boundary of the second site as a first boundary part, and to identify a partial boundary of the first site that is not adhere to the boundary of the second site as a second boundary part; and a correction unit configured to correct a shape of the first boundary part based on the second boundary part. 
     According to the present invention, a part of the boundary of the site included as the attached abnormal shadow in the medical image can be corrected to assist in diagnosis based on a shape of the tissue. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram for illustrating a system configuration (image processing system) of an image processing apparatus according to the present invention and devices connected thereto. 
         FIG. 2  is a diagram for illustrating a functional configuration of an image processing system including a control unit according to a first embodiment of the present invention. 
         FIG. 3  is a flow chart for illustrating processing executed by an image processing apparatus according to the first embodiment. 
         FIG. 4A  is a diagram for illustrating an example of a first boundary part. 
         FIG. 4B  is a diagram for illustrating an example of a second boundary part. 
         FIG. 5A  is a diagram for illustrating an example of the first boundary part. 
         FIG. 5B  is a diagram for illustrating a first example of correction of the first boundary part by a velocity function. 
         FIG. 5C  is a diagram for illustrating a second example of correction of the first boundary part by the velocity function. 
         FIG. 6A  and  FIG. 6B  are diagrams for illustrating correction of a shape of the first boundary part with an approximate ellipsoid. 
         FIG. 7A  and  FIG. 7B  are diagrams for illustrating an example in which the shape of the first boundary part is corrected based on a symmetrical image of the second boundary part. 
         FIG. 8A  and  FIG. 8B  are diagrams for illustrating an example in which the first boundary part is corrected based on a rotated image of the second boundary part. 
         FIG. 9  is a diagram for illustrating a functional configuration of an image processing system including a control unit according to a second embodiment of the present invention. 
         FIG. 10  is a flow chart for illustrating processing executed by an image processing apparatus according to the second embodiment. 
         FIG. 11A  and  FIG. 11B  are diagrams for illustrating determination on whether or not a first boundary part needs to be corrected. 
         FIG. 12  is a diagram for illustrating a functional configuration of an image processing system including a control unit according to a third embodiment of the present invention. 
         FIG. 13  is a flow chart for illustrating processing executed by an image processing apparatus according to the third embodiment. 
         FIG. 14A  and  FIG. 14B  are diagrams for illustrating estimation of a shape of a first site. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Preferred embodiments of the present invention will now be described in detail in accordance with the accompanying drawings. 
     First Embodiment 
     Now, an example of a first embodiment of the present invention is described in detail with reference to the drawings. An image processing apparatus according to this embodiment is configured to extract a candidate region (pulmonary nodule candidate region) of a pulmonary nodule, which is a site suspected to have a lung cancer (tumor), from a CT image (target image) of a target case, and to correct an attached surface (boundary surface that is adhere to a pleura) of a pulmonary nodule candidate region attached to the pleura. At this time, the image processing apparatus according to this embodiment is configured to identify the attached surface and a non-attached surface (boundary surface that is not adhere to the pleura) of the pulmonary nodule candidate region. Then, a shape of the attached surface is corrected based on a shape of the extracted non-attached surface. 
     In the following description, a case is described where a pulmonary nodule attached to a pleura on a chest CT image is a processing target, but a range of application of the present invention is not limited to a modality and a target, such as a pulmonary nodule or tumor, of this embodiment. The present invention is also applicable to another modality, such as MRI, and to sites, such as a cyst, an angioma, and hyperplasia other than the pulmonary nodule or tumor. 
     Next, specific apparatus configuration, functional configuration, and processing flow are described.  FIG. 1  is a diagram for illustrating a system configuration (image processing system) of the image processing apparatus according to this embodiment and devices connected thereto. The image processing system according to this embodiment includes an image processing apparatus  100 , an imaging device  110 , a data server  120 , a display unit (monitor)  160 , and input units (mouse  170  and keyboard  180 ). 
     The image processing apparatus  100  may be achieved with a personal computer (PC), for example, and includes a control unit (CPU)  11 , a main memory  12 , a magnetic disk  13 , and a display memory  14 . The control unit  11  is configured to mainly control operation of components of the image processing apparatus  100 . The main memory  12  is configured to store a control program to be executed by the control unit  11 , and to provide a work area for program execution by the control unit  11 . The magnetic disk  13  is configured to store an operating system (OS), device drivers for peripheral devices, and various types of application software including a program for performing the processing to be described later. 
     The display memory  14  is configured to temporarily store display data for the display unit  160 . The display unit  160  is a cathode ray tube (CRT) monitor or a liquid crystal monitor, for example, and is configured to display an image based on data from the display memory  14 . The mouse  170  and the keyboard  180 , which function as the input units, are used by a user to perform pointing input and input of characters and the like, respectively. Those components are communicably connected to one another by a common bus  18 . 
       FIG. 2  is a diagram for illustrating a functional configuration of the image processing system including the control unit  11  according to this embodiment. The control unit  11  according to this embodiment includes, as functional components, an image acquisition part  1000 , a target region extraction part  1010 , a target site extraction part  1020 , a boundary extraction part  1030 , a boundary identification part  1031 , a correction part  1040 , and a display control part  1050 . Moreover, the control unit  11  is communicably connected to the data server  120  and the display unit  160 . 
     The image processing apparatus  100  including the control unit  11  is configured to process a medical image, which is acquired by the imaging device  110 , such as a computed tomography apparatus (CT), a magnetic resonance imaging device (MRI), or a radiography apparatus (digital radiography) configured to capture a two-dimensional radiation image. In this embodiment, description is given using a medical image (CT image) taken by a computed tomography apparatus (CT). 
     The imaging device  110  is configured to transmit the taken CT image to the data server  120 . The data server  120  is configured to hold the image (CT image) taken by the imaging device  110 . The image processing apparatus  100  is configured to read necessary data from the data server  120  via a network, such as a local area network (LAN), to thereby acquire image data stored in the data server  120 . 
     Next, elements forming the control unit  11  of the image processing apparatus  100  are described. The image acquisition part  1000  in  FIG. 2  is configured to acquire a CT image (hereinafter referred to as “target image”) of a chest, which is associated with an object (target case) and is a target of the image processing apparatus  100 , from the data server  120 , and to load the CT image into the image processing apparatus  100 . 
     The target region extraction part  1010  is configured to extract a target region including a target site as a target of diagnosis. For example, the target region extraction part  1010  is configured to extract, from the target image acquired by the image acquisition part  1000 , a lung region including a pulmonary nodule, pleurae, pleural spaces, lung blood vessels, bronchi, and other such regions. The target site extraction part  1020  is configured to extract a target site (first site) and a site (second site) that is adhere to the target site in a medical image. For example, the target site extraction part  1020  is configured to extract, from the lung region extracted by the target region extraction part  1010 , at least one region (pulmonary nodule candidate region), which is a site suspected to have a pulmonary nodule. 
     The boundary extraction part  1030  is configured to extract a boundary of the target site (first site) and a boundary of the site (second site) that is adhere to the target site in the medical image. For example, the boundary extraction part  1030  is configured to extract, based on information on the lung region extracted by the target region extraction part  1010  and information on the pulmonary nodule candidate region extracted by the target site extraction part  1020 , a boundary of the pulmonary nodule candidate region and a boundary of a site, such as an organ or blood vessels, that is adhere to the pulmonary nodule candidate region. The boundary may be a boundary line or a boundary surface, or a collection of points forming the boundary line or the boundary surface. 
     The boundary identification part  1031  is configured to identify, as a first boundary part (attached part), a partial boundary of the target site (first site) that is adhere to the boundary of the site (second site) that is adhere to the target site. Then, the boundary identification part  1031  is configured to identify, as a second boundary part (non-attached part), a partial boundary of the target site (first site) that is not adhere to the boundary of the site (second site) that is adhere to the target site. For example, the boundary identification part  1031  is configured to identify an attached surface and a non-attached surface of the pulmonary nodule candidate region. 
     The correction part  1040  is configured to correct a shape of the attached part (first boundary part) of the target site based on the non-attached part (second boundary part) of the target site. For example, the correction part  1040  is configured to correct a shape of the attached surface based on information on the non-attached surface of the pulmonary nodule candidate region, which has been identified by the boundary identification part  1031 . The display control part  1050  is configured to output, to the display unit  160 , information, such as coordinates and an instruction to change a display format, on the target site (first site), the site (second site) that is adhere to the target site, the attached part (first boundary part) of the target site before and after the correction, and the non-attached part (second boundary part) of the target site. 
     For example, the display control part  1050  is configured to output, to the display unit  160 , the information on the pulmonary nodule candidate region extracted by the target site extraction part  1020 , the site that is adhere to the pulmonary nodule candidate region, the attached surface and the non-attached surface of the pulmonary nodule candidate region, which have been identified by the boundary identification part  1031 , and the attached surface corrected by the correction part  1040 . The display unit  160  is configured to display, based on the information, the target site (first site), the site (second site) that is adhere to the target site, the attached part (first boundary part) of the target site before and after the correction, and the non-attached part (second boundary part) of the target site. 
     Next, operation of the image processing apparatus  100  is described in detail with reference to  FIG. 3 .  FIG. 3  is a flow chart for illustrating processing executed by the image processing apparatus  100  according to this embodiment. This embodiment is realized by the control unit  11  executing a program (program for achieving the functional configuration of the image processing apparatus  100 ) stored in the main memory  12 . Moreover, results of processing performed by the image processing apparatus  100  are stored in the main memory  12  to be recorded. 
     Moreover, the present invention may also be realized by supplying the program for achieving the functional configuration of the image processing apparatus  100  to the system or the apparatus via a network or a recording medium, and allowing one or more processors in a computer of the system or the apparatus to read and execute the program. Further, the present invention may also be realized by a circuit (for example, an application specific integrated circuit: ASIC) that is configured to achieve one or more functions. 
     &lt;Step S 1100 &gt; 
     In Step S 1100 , the image acquisition part  1000  acquires a target image. The image acquisition part  1000  executes processing of acquiring, as the target image, a CT image of a target case from the data server  120 , and developing and holding the CT image on the main memory  12  of the image processing apparatus  100 . 
     The target image in this embodiment is formed of a plurality of pixels, which are identifiable by components in directions of three orthogonal axes (x, y, and z), and a pixel size as additional information is defined for each of the three axis directions. In this embodiment, an exemplary case is described where respective pixel sizes in the three axis directions are specifically r_size_x=1.0 mm, r_size_y=1.0 mm, and r_size_z=1.0 mm. A density value (pixel value) of the target image may be regarded as a function derived with reference to a pixel address in a three-dimensional array of pixels. 
     In this embodiment, the target image is expressed as a function I(x,y,z). The function I(x,y,z) is a function using three-dimensional real-space coordinate values (x,y,z) in an imaging region of the target image as arguments to express a pixel value at the position. 
     &lt;Step S 1110 &gt; 
     In Step S 1110 , the target region extraction part  1010  extracts a target region (lung region). The target region extraction part  1010  extracts a region including an air region, a part of a bronchial region, a part of a lung blood vessel region, and the pulmonary nodule candidate region, that is, a region anatomically recognized as a lung field. 
     The target region extraction part  1010  is capable of automatically extracting a lung region V lung  from the function I(x,y,z) using a method using thresholding, region extension processing, level sets, or an organ atlas (model) based on anatomy, for example. Alternatively, a user may make a manual modification or adjustment to the lung region V lung  via the mouse  170 . 
     &lt;Step S 1120 &gt; 
     In Step S 1120 , the target site extraction part  1020  extracts a target site (pulmonary nodule candidate region) and a site, such as an organ or blood vessels, that is adhere to the target site. The target site extraction part  1020  extracts, from the lung region V lung  extracted in Step S 1110 , at least one pulmonary nodule candidate region based on a density value (pixel value) or a shape having a feature of the pulmonary nodule candidate region. 
     The target site extraction part  1020  detects the pulmonary nodule candidate region from the lung region V lung  using a technology such as filtering for emphasizing a blob structure based on the thresholding or an eigenvalue of a Hessian matrix. Then, the target site extraction part  1020  extracts the pulmonary nodule candidate region more finely using an active contour model, such as level sets and snakes, graph cuts, and other such technologies on the detected pulmonary nodule candidate region. 
     Alternatively, the target site extraction part  1020  may extract the target site based on a particular pulmonary nodule candidate region specified by a user via the mouse  170 . In this case, the user uses the mouse  170  to obtain, as a seed point, coordinates belonging to the pulmonary nodule candidate region by referring to axial, sagittal, and coronal images of the target image displayed on the display unit  160 . 
     The target site extraction part  1020  extracts at least one target site by the level sets, the graph cuts, the active contour model, and other such technologies using information on the obtained seed point, information on the lung region V lung , and information on the density value (pixel value) or the shape having the feature of the pulmonary nodule candidate region. 
     Sites that are adhere to the pulmonary nodule (target site) include a pleura region, a blood vessel region, and the like. The pleura region is adhere to the lung region, and hence when the target region is adhere to the pleura region, the lung region V lung  may be acquired as the site (second site) that is adhere to the target site (first site). When the target site is adhere to blood vessels, there is a need to extract the blood vessel region in advance to be acquired as the second site. The blood vessel region may be extracted from the lung region V lung  by the active contour model, such as the level sets and the snakes, the graph cuts, and other such technologies based on a density value (pixel value) or a shape having a feature of the blood vessel region. 
     &lt;Step S 1130 &gt; 
     In Step S 1130 , the boundary extraction part  1030  extracts a boundary of the target site (first site) and a boundary of the site (second site) that is adhere to the target site. In this embodiment, a case is described where the second site is the lung region V lung . 
     Here, a pulmonary nodule candidate region, which is the target site extracted by the target site extraction part  1020 , is denoted by V nodule . The boundary extraction part  1030  extracts a boundary surface S nodule  of the pulmonary nodule candidate region (first site) V nodule , which is formed of a collection or series of boundary pixels of the pulmonary nodule candidate region V nodule . Specifically, the boundary extraction part  1030  extracts the boundary surface S nodule  by detecting pixels demarcating the pulmonary nodule candidate region V nodule  and a background in the pulmonary nodule candidate region V nodule . Moreover, the boundary extraction part  1030  similarly extracts a boundary surface S lung  of the lung region V lung  (second site), which is formed of a collection or series of boundary pixels of the lung region V lung . 
     &lt;Step S 1131 &gt; 
     In Step S 1131 , the boundary identification part  1031  identifies an attached part (first boundary part) and a non-attached part (second boundary part) of the target site. The boundary identification part  1031  identifies an attached surface S attached  and a non-attached surface S non-attached  of the pulmonary nodule candidate region V nodule  based on the boundary surface S nodule  of the pulmonary nodule candidate region V nodule  and the boundary surface S lung  of the lung region V lung , which have been extracted in Step S 1130 . 
     Here, the attached surface (first boundary part) S attached  is a surface at which the boundary surface S nodule  of the pulmonary nodule candidate region (first site) V nodule  is adhere to the boundary surface S lung  of the lung region V lung  (second site). Moreover, the non-attached surface (second boundary part) S non-attached  is a surface at which the boundary surface S nodule  of the pulmonary nodule candidate region (first site) V nodule  is not adhere to the boundary surface S lung  of the lung region V lung  (second site). 
     The boundary surface S lung  of the lung region V lung  is adhere to the pleura region, and hence the attached surface S attached  of the pulmonary nodule candidate region V nodule  may be regarded as a portion at which the pulmonary nodule candidate region V nodule  is adhere to the pleura region. In other words, the pulmonary nodule candidate region V nodule  with the attached surface S attached  is included as an attached abnormal shadow in the medical image. In contrast, the pulmonary nodule candidate region V nodule  without the attached surface S attached  is a solitary pulmonary nodule candidate region, and is included as a non-attached abnormal shadow in the medical image. 
       FIG. 4A  and  FIG. 4B  are diagrams for illustrating examples of an attached surface (first boundary part) and non-attached surfaces (second boundary parts). Cross sections are used to simplify the description. In  FIG. 4A , a pulmonary nodule candidate region  505 , which is attached to a pleura, is illustrated. In  FIG. 4B , a pulmonary nodule candidate region  515  of a solitary pulmonary nodule, which is not attached to the pleura, is illustrated. 
     A boundary surface of the pulmonary nodule candidate region  505  is formed of an attached surface (first boundary part)  506 , which is indicated by the broken line, and a non-attached surface (second boundary part)  507 , which is indicated by the solid line. A boundary surface of the pulmonary nodule candidate region  515  of the solitary pulmonary nodule is formed of a non-attached surface  517 . The boundary identification part  1031  identifies the attached surface (first boundary part)  506  and the non-attached surface (second boundary part)  507 . Moreover, the boundary identification part  1031  identifies the non-attached surface  517 . 
     &lt;Step S 1140 &gt; 
     In Step S 1140 , the correction part  1040  corrects a shape of the attached surface (first boundary part)  506  based on the non-attached surface (second boundary part)  507 . The correction part  1040  corrects the shape of the attached surface  506  of the pulmonary nodule candidate region  505  using the pulmonary nodule candidate region  505 , which has been extracted in Step S 1120 , and information (attached/non-attached-surface information) on the attached surface  506  and the non-attached surface  507 , which have been extracted in Step S 1131 . For the pulmonary nodule candidate region  515  of the solitary pulmonary nodule, no attached surface S attached  is identified, and hence the correction part  1040  does not perform processing of correcting the boundary of the pulmonary nodule candidate region  515 . 
     The correction part  1040  corrects (deforms) the shape of the attached surface  506  based on a shape feature of the non-attached surface  507  of the pulmonary nodule candidate region  505 . For example, the correction part  1040  corrects (deforms) the shape of the attached surface (first boundary part)  506  using the active contour model that is based on a shape (for example, curvature) of at least a part of the non-attached surface (second boundary part)  507 . In this embodiment, the attached surface  506  is corrected by being subjected to boundary surface propagation processing by a level set method, which is an active contour model. 
     The level set method is a phase-variable active contour model, and may be used to deform (propagate) a contour (boundary) to an ideal shape or position by using a cost function based on a feature amount representing a shape of the contour or a texture of a region. 
     Specifically, in the level set method, a space (high-dimensional space) that is one dimension higher is constructed for a target space, and a contour of a target is regarded as a cross section (zero iso-surface φ=0) of an implicit function φ defined in the high-dimensional space. The contour (zero iso-surface φ=0) of the target is deformed while a shape of the implicit function φ is moved along with time t. Therefore, when the implicit function φ is designed appropriately depending on a target shape feature (target region) of deformation of the target, a change in topology of the contour (boundary) of the target and occurrence of a singular point may be addressed. 
     In the level set method, in order to change a shape of the zero iso-surface, a speed of motion (movement) is given to a point belonging to the implicit function φ to propagate the implicit function φ with elapse of time t. This may generally be expressed with the following expression (1).
 
φ t   =−F·|∇φ|   (1)
 
     In the expression (1), F represents a velocity function (cost function). The speed of motion of the point belonging to the implicit function φ is determined by the velocity function F. In general, the velocity function F is designed in consideration of a texture or the shape feature of the target on the image so that the speed becomes lower, that is, the cost becomes larger as the zero iso-surface becomes farther from the contour of the target region, and so that the speed becomes higher, that is, the cost becomes smaller as the zero iso-surface becomes closer to the contour of the target region. Moreover, the velocity function F is designed so that the zero iso-surface is extended when the zero iso-surface is inside the target region, and so that the zero iso-surface is reduced when the zero iso-surface is outside the target region. 
     In this embodiment, the correction part  1040  corrects (deforms) the attached surface  506  so that a shape feature of the attached surface  506  approaches a shape feature of the non-attached surface  507  while maintaining smoothness of the contour (boundary) at points  520  of contact between the attached surface  506  and the non-attached surface  507 . Clinical findings suggest that growth of a pulmonary nodule region stops at the attached surface, and hence the attached surface has a shape that is different from that of the non-attached surface. Therefore, the correction part  1040  performs correction to extend the attached surface. In this embodiment, the attached surface  506  is extended using a velocity function F κ (i,j,k) based on the curvature, which is expressed by the following expression (2).
 
 F   κ ( i,j,k )=1− e   α·|κ(i,j,k)−κ0|   (2)
 
     Here, a pixel (i,j,k) is a pixel (neighboring pixel) that is located outside the boundary surface S nodule  (attached surface  506  and non-attached surface  507 ) of the pulmonary nodule candidate region  505  and is adjacent to the attached surface  506 , and is a pixel (extension candidate pixel) for extending the attached surface  506 . 
     In the expression (2), (i,j,k) represents a coordinate position of the pixel. Moreover, κ(i,j,k) represents a curvature at the pixel (i,j,k), and is calculated by a signed distance function with the zero iso-surface φ=0 being a foreground. Further, κ 0  represents a curvature feature amount of the non-attached surface  507 , and is an average (average curvature) of curvatures at respective pixels on the non-attached surface  507 , for example. Alternatively, κ 0  may be a curvature at a pixel closest to the pixel (i,j,k) of a collection of reference pixels, the reference pixels being pixels on the non-attached surface  507  that is adhere to the attached surface  506 . 
     In the expression (2), α (α&lt;0) is a weight coefficient. With the velocity function F κ (i,j,k), as a difference between the curvature κ(i,j,k) at the pixel (i,j,k) and κ 0  becomes larger, speeds F κ  at extension candidate pixels become higher, and a degree of extension of the first boundary part (attached surface  506 ) becomes higher. In contrast, as the difference between the curvature κ(i,j,k) at the pixel (i,j,k) and κ 0  becomes smaller, the speeds F κ  at the extension candidate pixels become lower to approach to 0, and the degree of extension of the first boundary part (attached surface  506 ) becomes lower. 
     In order to extend the first boundary part (attached surface  506 ) more efficiently and make the extension easier to control, a limitation expressed by the following expression (3) may be added.
 
 N   attached   ≤β·N   non-attached   (3)
 
     In the expression (3), β represents a coefficient. N attached  represents the number of pixels of the first boundary part (attached surface  506 ). N non-attached  represents the number of pixels of the second boundary part (non-attached surface  507 ). With the expression (3), the number of pixels of the first boundary part (attached surface  506 ), which is extended by the correction, may be limited based on the number of pixels of the second boundary part (non-attached surface  507 ) to avoid infinite extension of the first boundary part (attached surface  506 ). 
       FIG. 5A  to  FIG. 5C  are diagrams for illustrating an example of correction of the first boundary part (attached surface) by the velocity function F κ (i,j,k). In this example, one of an average curvature at pixels (pixels indicated by black) of the non-attached surface  527  and a curvature at one of reference pixels  523  (pixels indicated by “=”) is κ 0 . Neighboring pixels of pixels (pixels indicated by gray) of the attached surface  526  are extension candidate pixels  524  (pixels indicated by “*”) ( FIG. 5A ). 
     The correction part  1040  calculates differences between curvatures κ(i,j,k) and κ 0  at the extension candidate pixels  524 , and the attached surface  526  is extended to an attached surface  528  in order from a pixel having the largest difference ( FIG. 5B ). This processing is repeated to extend the attached surface  528  to an attached surface  529  ( FIG. 5C ). As the speeds F κ  at the extension candidate pixels become lower to approach to 0, and there is no need to further extend the attached surface  529 , the correction (extension) of the attached surface  526  ends. The correction part  1040  sets the attached surface  529  obtained at the end of the correction processing as an attached surface after the correction. 
     Alternatively, the correction part  1040  may correct the shape of the attached surface (first boundary part) based on at least one of an approximate curve approximating the non-attached surface (second boundary part), an approximate curved surface approximating the non-attached surface (second boundary part), a symmetrical image of the non-attached surface (second boundary part), or a rotated image of the non-attached surface (second boundary part). 
     For example, the correction part  1040  may correct the shape of the attached surface (first boundary part) using ellipsoid fitting and other such technologies with an approximate ellipsoid (approximate curved surface) approximating the non-attached surface (second boundary part).  FIG. 6A  and  FIG. 6B  are diagrams for illustrating correction of the shape of the attached surface (first boundary part) with the approximate ellipsoid (approximate curved surface). As illustrated in  FIG. 6A  and  FIG. 6B , the correction part  1040  calculates an approximate ellipsoid E(x,y,z) including reference pixels  533 , the reference pixels  533  being pixels on a non-attached surface  537  that is adhere to an attached surface  536 . 
     In this case, the correction part  1040  may further select one or more pixels as reference pixels  534  from the non-attached surface  537  to calculate the approximate ellipsoid E(x,y,z) including the reference pixels  533  and the reference pixels  534 . Alternatively, the correction part  1040  may calculate the approximate ellipsoid E(x,y,z) by a least squares error method based on the reference pixels  533  and the reference pixels  534 . The correction part  1040  sets a part (boundary) of an approximate ellipsoid  538  on the attached surface side of the reference pixels  533  as an attached surface  539  after the correction. 
     &lt;Step S 1150 &gt; 
     In Step S 1150 , the display unit  160  displays at least one piece of information, such as coordinates and an instruction to change a display format, on the target site (first site), the site (second site) that is adhere to the target site, the attached part (first boundary part) of the target site before and after the correction, and the non-attached part (second boundary part) of the target site. In this case, the display control part  1050  transmits, to the display unit  160 , information on the pulmonary nodule candidate region (first site), the lung region (second site), the attached surface (first boundary part) before and after the correction, and the non-attached surface (second boundary part). 
     The display control part  1050  may superimpose those pieces of information, such as the image, on the medical image, such as the CT image, for display on the display unit  160 . In this case, the display control part  1050  may generate a three-dimensional medical image on which those pieces of information are superimposed by volume rendering for display on the display unit  160 . Alternatively, the display control part  1050  may generate a predetermined cross-sectional image of the superimposed three-dimensional medical image for display on the display unit  160 . 
     As described above, according to this embodiment, the image processing apparatus  100  may identify the attached surface and the non-attached surface of the site included as the attached abnormal shadow in the medical image, and correct the attached surface based on features of the attached surface and the non-attached surface. As a result, a shape feature amount, which is to be used in computer aided diagnosis (including a technology for automatically detecting an abnormal shadow region and a technology for distinguishing benignancy and malignancy of a tissue), may be appropriately calculated for a site suspected to be an abnormal tissue, which can contribute to improvement in performance of the computer aided diagnosis. 
     The first embodiment is described above. However, the present invention is not limited thereto, and modifications and alterations may be made within the scope defined in the claims. For example, the correction part  1040  may correct the shape of the attached surface (first boundary part) based on at least one of a symmetrical image (mirror image) of the non-attached surface (second boundary part) or a rotated image of the non-attached surface (second boundary part) in addition to the approximate curve and the approximate curved surface. 
       FIG. 7A  and  FIG. 7B  are diagrams for illustrating an example in which the shape of the attached surface (first boundary part) is corrected based on the symmetrical image of the non-attached surface (second boundary part). The correction part  1040  sets a surface obtained by approximating the attached surface  546  with a plane as a plane of symmetry to generate a symmetrical image of the non-attached surface  547 , and sets the generated symmetrical image as an attached surface  548  after the correction. 
     Moreover,  FIG. 8A  and  FIG. 8B  are diagrams for illustrating an example in which the attached surface (first boundary part) is corrected based on the rotated image of the non-attached surface (second boundary part). The correction part  1040  rotates an attached surface  548  (plane symmetrical image of the non-attached surface) of  FIG. 7B  by 180° with a rotation axis  554 , which is a normal vector at a center  553  of the surface obtained by approximating an attached surface  556  with a plane, to thereby generate a rotated image. The correction part  1040  connects the rotated image as a non-attached surface  557  to set the rotated image to an attached surface  558  after the correction. 
     As described above, the correction part  1040  may correct the attached surface (first boundary part) of the target site (first site) in the medical image using at least one of the approximate curve, the approximate curved surface, the symmetrical image, or the rotated image to a shape approximating the non-attached surface (second boundary part), and hence calculate the shape feature amount of the target site more appropriately. 
     Second Embodiment 
     Next, an example of a second embodiment of the present invention is described in detail with reference to the drawings. Description on configurations, functions, and operation similar to those of the first embodiment is omitted, and a configuration unique to this embodiment is mainly described. 
     In the second embodiment, there is described an example including additional processing of determining whether or not to perform processing for correcting the attached surface, based on the attached/non-attached-surface information identified by the boundary identification part  1031  in Step S 1131 . In this embodiment, it is determined whether or not to perform the correction processing based on the shape feature amount of at least one of the attached surface or the non-attached surface. When it is determined to perform the correction processing, the correction part  1040  corrects the attached surface as in the first embodiment. 
     Next, an apparatus configuration, a functional configuration, and a processing flow are specifically described.  FIG. 9  is a diagram for illustrating a functional configuration of an image processing system including a control unit  111  according to this embodiment. As illustrated in  FIG. 9 , the control unit  111  in this embodiment includes, as functional components, an image acquisition part  1000 , a target region extraction part  1010 , a target site extraction part  1020 , a boundary extraction part  1030 , a boundary identification part  1031 , a determination part  1035 , a correction part  1040 , and a display control part  1050 . 
     The determination part  1035  sets a feature amount (shape feature amount) representing at least one feature of the shape of the attached surface (first boundary part) or the non-attached surface (second boundary part) as a determination feature amount, and determines whether or not to correct the shape of the attached surface (first boundary part), based on the determination feature amount. When the determination part  1035  determines that the correction processing is to be performed, the correction part  1040  corrects the shape of the attached part (first boundary part) of the target site based on the non-attached part (second boundary part) of the target site. 
     Next, operation of an image processing apparatus according to this embodiment is described in detail with reference to  FIG. 10 .  FIG. 10  is a flow chart for illustrating processing executed by the image processing apparatus according to this embodiment. Processing in Steps S 2000  to S 2030  corresponds to processing in Steps S 1100  to S 1130  in the first embodiment. 
     &lt;Step S 2035 &gt; 
     In Step S 2035 , the determination part  1035  determines whether or not to correct the shape of the attached surface (first boundary part), based on the determination feature amount. Here, the determination feature amount is at least one of a length, a tangent, a normal, or a curvature of the attached surface (first boundary part) or of the non-attached surface (second boundary part). Alternatively, the determination feature amount may be at least one of a center of gravity, an area, a volume, a circularity, or a sphericity of the region defined by the attached surface (first boundary part) or of the non-attached surface (second boundary part). 
     For example, the determination part  1035  compares the determination feature amounts of the attached surface and the non-attached surface, which have been acquired in Step S 2030 , to determine whether or not to correct the attached surface. The determination part  1035  calculates respective areas of regions defined by the attached surface and the non-attached surface, and calculates a ratio D f  of the areas of the attached surface and the non-attached surface. When D f  is equal to or more than a preset threshold T f , the determination part  1035  determines that the attached surface is to be corrected, and the processing proceeds to Step S 2040 . In contrast, when D f  is less than the threshold T f , the determination part  1035  determines that the attached surface is not to be corrected, and the processing proceeds to Step S 2050 . 
     As illustrated in  FIG. 11A , the determination part  1035  compares an area of an attached surface  566  and an area of a non-attached surface  567  in a pulmonary nodule candidate region (first site)  565  to calculate the ratio D f  of the areas. When the pulmonary nodule candidate region (first site)  565  is hardly attached to the pleura (second site), D f  is less than the threshold T f , and hence the determination part  1035  determines that the attached surface  566  is not to be corrected. 
     Alternatively, as illustrated in  FIG. 11B , the determination part  1035  compares an average curvature of the attached surface  566  and an average curvature of the non-attached surface  567  of the pulmonary nodule candidate region (first site)  565  to calculate a difference S f  between the average curvatures. In a pulmonary nodule candidate region (first site)  575  having a polygonal or polyhedral contour, an average curvature of an attached surface  576  and an average curvature of a non-attached surface  577  are substantially equal to each other (close to 0), and hence the difference S f  is less than the threshold T f , with the result that the determination part  1035  determines that the attached surface  576  is not to be corrected. 
     Alternatively, the determination part  1035  may compare a length of a cross section of the attached surface with the predetermined threshold T f , or compare a ratio of lengths of cross sections of the attached surface and the non-attached surface with the predetermined threshold T f . Alternatively, the determination part  1035  may generate a histogram of directions of normals of the non-attached surface, and determine whether or not to correct the attached surface based on a distribution of the histogram. Alternatively, the determination part  1035  may determine whether or not to correct the attached surface based on a circularity of the attached surface or on a sphericity of the pulmonary nodule candidate region (region defined by the attached surface and the non-attached surface). 
     As described above, the determination part  1035  determines the influence the correction of the attached surface (first boundary part) has on the calculation of the shape feature amount used in the computer aided diagnosis. When the influence is small, the attached surface is not corrected so that more efficient processing (processing of calculating the shape feature amount) may be performed without performing unnecessary correction processing. 
     &lt;Step S 2040 &gt; 
     In Step S 2040 , the correction part  1040  corrects the shape of the attached surface (first boundary part), which has been determined to be corrected, based on the non-attached part (second boundary part). The correction part  1040  corrects the shape of the attached surface of the pulmonary nodule candidate region using the lung region extracted in Step S 2010 , the pulmonary nodule candidate region extracted in Step S 2020 , and the attached/non-attached-surface information identified in Step S 2031 . 
     &lt;Step S 2050 &gt; 
     In Step S 2050 , the display unit  160  displays at least one of the target site (first site), the site (second site) that is adhere to the target site, or the attached/non-attached-surface information under control of the display control part  1050 . When it is determined in Step S 2035  that the attached surface is not to be corrected, the display unit  160  may display information indicating that the attached surface has not been corrected. 
     As described above, the determination part  1035  determines whether or not the attached surface needs to be corrected, based on the determination feature amount so that the more efficient processing (processing of calculating the shape feature amount) may be performed without performing unnecessary correction processing. 
     Third Embodiment 
     Next, an example of a third embodiment of the present invention is described in detail with reference to the drawings. Description on configurations, functions, and operation similar to those of the first and second embodiments is omitted, and a configuration unique to this embodiment is mainly described. 
     In the third embodiment, there is described an example in which the shape of the pulmonary nodule candidate region (first site) is estimated based on shape feature amounts of the attached surface (first boundary part) before and after the correction. 
     Next, an apparatus configuration, a functional configuration, and a processing flow are specifically described.  FIG. 12  is a diagram for illustrating a functional configuration of an image processing system including a control unit  1111  according to this embodiment. As illustrated in  FIG. 12 , the control unit  1111  in this embodiment includes, as functional components, an image acquisition part  1000 , a target region extraction part  1010 , a target site extraction part  1020 , an estimated feature amount calculation part  1025 , a boundary extraction part  1030 , a boundary identification part  1031 , a correction part  1040 , a shape estimation part  1045 , and a display control part  1050 . 
     The estimated feature amount calculation part  1025  is configured to calculate, as an estimated feature amount, a feature amount of the attached surface (first boundary part) representing a feature of a shape of the boundary of the pulmonary nodule candidate region (first site) before the correction. The shape estimation part  1045  is configured to calculate, as an estimated feature amount, a feature amount of the attached surface (first boundary part) representing at least one feature of the shape of the boundary of the pulmonary nodule candidate region (first site) after the correction. Here, the estimated feature amount before the correction is represented by f before , and the estimated feature amount after the correction is represented by f after . 
     The shape estimation part  1045  is configured to set the feature amounts (shape feature amounts) representing the at least one feature of the shape of the boundary of the pulmonary nodule candidate region (first site) before the correction and the boundary of the pulmonary nodule candidate region after the correction as the estimated feature amounts, and to estimate the shape of the pulmonary nodule candidate region based on the estimated feature amounts. 
     Next, operation of an image processing apparatus according to this embodiment is described in detail with reference to  FIG. 13 .  FIG. 13  is a flow chart for illustrating processing executed by the image processing apparatus according to this embodiment. Processing in Steps S 3000  to S 3020  corresponds to processing in Steps S 1100  to S 1120  in the first embodiment. 
     &lt;Step S 3025 &gt; 
     In Step S 3025 , the estimated feature amount calculation part  1025  calculates the estimated feature amount f before , which is the shape feature amount of the pulmonary nodule candidate region before the correction, using the density value (pixel value) of the target image acquired in Step S 3000  and information on the target site (first site) extracted in Step S 3020 . Here, the estimated feature amount f before  is at least one of a length, a tangent, a normal, a curvature, a center of gravity, an area, a volume, an elongatedness, a circularity, a sphericity, a moment, a Fourier descriptor, or spherical harmonics (SPHARM) of the boundary of the pulmonary nodule candidate S 3000  region (first site) before the correction. 
     &lt;Step S 3030 &gt; 
     In Step S 3030 , the boundary extraction part  1030  extracts the boundary of the target site (first site) and the boundary of the site (second site) that is adhere to the target site to obtain the attached/non-attached-surface information. 
     &lt;Step S 3040 &gt; 
     In Step S 3040 , the correction part  1040  corrects the shape of the attached part (first boundary part) based on at least one of the approximate curve approximating the non-attached surface (second boundary part), the approximate curved surface approximating the non-attached surface (second boundary part), the symmetrical image of the non-attached surface (second boundary part), or the rotated image of the non-attached surface (second boundary part). The correction part  1040  may select the at least one of the approximate curve, the approximate curved surface, the symmetrical image, or the rotated image for correcting the shape of the attached part (first boundary part), based on the estimated feature amount f before  calculated in Step S 3025 . 
     For example, the estimated feature amount calculation part  1025  calculates, as the estimated feature amounts f before , an area (attached area) of a surface obtained by approximating the attached surface S attached  with a plane and a cross-sectional area (cross-section) of the pulmonary nodule candidate region (first site) taken along a plane parallel to the surface. When the attached area is larger than any cross-sectional area, that is, the attached area is largest at the first site, the correction part  1040  selects the symmetrical image or the rotated image of the non-attached surface (second boundary part), and corrects the shape of the attached part (first boundary part) based on the selected symmetrical image or rotated image. 
     &lt;Step S 3045 &gt; 
     In Step S 3045 , the shape estimation part  1045  calculates the estimated feature amount f after , which is the shape feature amount of the pulmonary nodule candidate region (first site) after the correction, using the information on the target site (first site) corrected in Step S 3040 . Moreover, the shape estimation part  1045  estimates the shape of the pulmonary nodule candidate region (first site) based on estimated feature amounts f before  and f after  before and after the correction. 
     Here, the estimated feature amount f after  is at least one of a length, a tangent, a normal, a curvature, a center of gravity, an area, a volume, an elongatedness, a circularity, a sphericity, a moment, a Fourier descriptor, or spherical harmonics (SPHARM) of the boundary of the pulmonary nodule candidate region (first site) after the correction. 
     For example, the shape estimation part  1045  compares the estimated feature amounts f before  and f after  before and after the correction to estimate the shape of the pulmonary nodule candidate region (first site). The shape estimation part  1045  estimates the shape of the pulmonary nodule candidate region (first site) based on the estimated feature amounts f before  and f after  before and after the correction, and on a difference f sub  between the estimated feature amounts f before  and f after . 
     As illustrated in  FIG. 14A , the estimated feature amount calculation part  1025  sets the sphericity of the pulmonary nodule candidate region (first site)  585  before the correction as the shape feature amount to calculate the estimated feature amount f before . A region defined by the non-attached surface  587  is semispherical, and hence the estimated feature amount (sphericity) f before  is 0.5 or less. 
     The shape estimation part  1045  calculates, as the estimated feature amount f after , a sphericity of the pulmonary nodule candidate region (first site)  585  after the correction. As a result of the correction, an attached surface  586  is extended to an attached surface  588  to the pulmonary nodule candidate region after the correction spherical, and hence the estimated feature amount (sphericity) f after  is 0.8 or more. In this case, the shape estimation part  1045  estimates that the non-attached surface  587  is semispherical based on the estimated feature amounts f before  and f after  before and after the correction. 
     Moreover, as illustrated in  FIG. 14B , the estimated feature amount calculation part  1025  sets a sphericity of a pulmonary nodule candidate region (first site)  595  before the correction as the shape feature amount to calculate the estimated feature amount f before . A non-attached surface  597  is rectangular parallelepiped, and hence the estimated feature amount (sphericity) f before  is 0.5 or less. 
     The shape estimation part  1045  calculates, as the estimated feature amount f after , a sphericity of a pulmonary nodule candidate region (first site)  595  after the correction. As a result of the correction, an attached surface  596  is extended to an attached surface  598  to make the pulmonary nodule candidate region after the correction rectangular parallelepiped or cubic, and hence the estimated feature amount (sphericity) f after  is 0.5 or less. Moreover, the sphericities before and after the correction are substantially the equal to each other, and hence the difference f sub  between the estimated feature amounts f before  and f after  is a predetermined threshold or less. In this case, the shape estimation part  1045  estimates that a non-attached surface  597  is rectangular parallelepiped (polyhedral) based on the estimated feature amounts f before  and f after  before and after the correction, and on the difference f sub . 
     Alternatively, the shape estimation part  1045  may generate a histogram of directions of normals of the non-attached surface, and estimate the shape of the pulmonary nodule candidate region (first site) based on a distribution of the histogram. Alternatively, the shape estimation part  1045  may estimate the shape of the pulmonary nodule candidate region (first site) based on a change in cross-sectional areas of the pulmonary nodule candidate region (first site) taken along a plurality of parallel planes. Alternatively, the shape estimation part  1045  may estimate the shape of the pulmonary nodule candidate region (first site) based on a shape vector of the spherical harmonics (SPHARM). 
     Alternatively, the shape estimation part  1045  may estimate the shape of the above-mentioned first site by pattern recognition. When the pulmonary nodule candidate region included as the attached abnormal shadow in the medical image is to be classified into categories, such as sphere-like objects or polygonal, the shape estimation part  1045  sets shape feature amounts belonging to the respective categories as learning data in advance, and generates a discriminative model using the estimated feature amounts f before  and f after  and the difference f sub . The shape estimation part  1045  uses the discriminative model for pattern recognition to estimate the shape of the pulmonary nodule candidate region (first site). 
     &lt;Step S 3050 &gt; 
     In Step S 3050 , the display unit  160  displays at least one of the target site (first site), the site (second site) that is adhere to the target site, the attached/non-attached-surface information, the estimation result of the shape of the target site (first site), the estimated feature amounts f before  and f after , or the difference f sub  under control of the display control part  1050 . 
     As described above, the shape estimation part  1045  estimates the shape of the pulmonary nodule candidate region (first site) so that the estimated shape is an indicator in the processing for distinguishing benignancy and malignancy of the pulmonary nodule. Moreover, the shape feature amount and the estimated feature amounts of the region of the attached abnormal shadow, which have been calculated by the CAD, may be compared to determine whether or not the correction by the correction part  1040  has been appropriate. Further, the estimated shape may be used as a new feature amount in the CAD. 
     In another embodiment of the present invention, it is determined whether or not the target region, such as the pulmonary nodule candidate region, includes the attached part, and when it is determined that the attached part is included, the shape of the target region including the attached part is corrected. In this case, as in the first embodiment, for the target region extracted by the target region extraction part  1010 , the boundary between the target region and the site, such as an organ or blood vessels, that is adhere to the target region is extracted by the boundary extraction part  1030 . Then, the boundary identification part  1031  identifies, as the first boundary part (attached part), the partial boundary of the target site (first site) that is adhere to the boundary of the site (second site) that is adhere to the target site. At this time, when the attached part is identified by the boundary identification part  1031 , the boundary identification part  1031  determines that there is an attached part. When the determination is made, the correction part  1040  corrects the shape of the attached part (first boundary part) of the target site based on the non-attached part (second boundary part) of the target site. Here, when no attached part is identified by the boundary identification part  1031 , the correction part  1040  does not perform the above-mentioned correction processing. 
     The display control part  1050  outputs, to the display unit  160 , the information on the pulmonary nodule candidate region extracted by the target site extraction part  1020 , the site that is adhere to the pulmonary nodule candidate region, the attached surface and the non-attached surface of the pulmonary nodule candidate region, which have been identified by the boundary identification part  1031 , and the attached surface corrected by the correction part  1040 . Here, when the correction has been performed by the correction part  1040 , information indicating that the correction has been performed may be output. Moreover, when the correction has been performed by the correction part  1040 , the display control part  1050  may cause the shape after the correction to be displayed in a form of being superimposed on the target region. Moreover, for example, the display of the boundary of the target region after the correction and the display of the boundary of the target region before the correction may be displayed switchably. Alternatively, information indicating a position of the first boundary part (attached part) and information indicating the shape after the correction may be superimposed on the target image to establish a state in which activity indication is displayed. 
     Moreover, when the correction has not been performed by the correction part  1040 , information indicating that the correction has not been performed may be output. 
     Moreover, in another embodiment of the present invention, the display control part  1050  may cause the target image to be displayed, cause the first boundary part (attached part) and the non-attached part (second boundary part), which have been identified by the boundary identification part  1031 , to be superimposed on the target region of the target image for display, and allow the first and second boundary parts to be changed in response to an operation input from operation units, such as the mouse  170  and the keyboard  180 . When the first or second boundary part has been changed in response to the operation input, the processing for correcting the shape by the correction part  1040  and the determination processing by the determination part  1035  are performed based on the boundary part after the change. When the change in response to the operation input has been made after the processing for correcting the shape by the correction part  1040  or the determination processing by the determination part  1035 , the correction part  1040  and the determination part  1035  respectively perform the correction processing and the determination processing again. 
     The shape corrected in the processing according to the embodiments described above is used in the processing for deriving image findings of the target site. The image findings are, for example, text information that is given by an image diagnostic doctor and indicates evaluations on a lesion and the like. In one of the embodiments, such image findings are automatically derived by the system. For example, morphological image findings on a general shape of the target site, such as sphere-like objects or lobular, and on a contour line, such as smooth or convex, are derived using the shape feature amount calculated from the target site (first site) after the above-mentioned correction. With the above-mentioned correction processing, the image findings may be derived more correctly as compared to a case where the correction processing is not performed. The derivation processing is achieved by using mapping with a reference value to which the shape feature amount has been set in advance, classification by machine learning, and other such technologies, for example. The morphological image findings are used as important bases in the image diagnosis, and hence are expected to improve the accuracy in diagnosis of the lesion. 
     Other Embodiments 
     Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2015-151611, filed Jul. 31, 2015, which is hereby incorporated by reference herein in its entirety.