Patent Publication Number: US-8538113-B2

Title: Image processing device and method for processing image to detect lesion candidate region

Description:
TECHNICAL FIELD 
     The present invention relates to an image processing device and the like for processing an image to be used for medical diagnostic purposes. 
     BACKGROUND ART 
     Tomographic images and the like of subjects, taken by a variety of devices such as the X-ray CT (computed tomography) machines and the MRI (magnetic resonance imaging) machines, have been conventionally known as images to be used for medical diagnostic purposes. Meanwhile, the computer-aided detections (hereinafter referred to as CADs) have been developed for: analyzing the aforementioned medical images with use of computers; detecting lesion candidates from shadows in the medical images; and providing a doctor with the detected lesion candidates. The CADs automatically detect regions expected as lesions in the images (hereinafter referred to as lesion candidate regions) based on shape properties and concentration properties of the lesions for reducing the burden of doctors. 
     For example, some of lesion candidates (e.g., polyps in regions of the large intestine) are formed in spherical shapes and thus have unique shape properties. In Patent Document 1, for instance, a curvature value typified by the shape index or the like is calculated as a feature amount indicating a shape property and abnormal shadow candidate regions are narrowed down based on the shape of a curved surface indicating concentration distribution of an image. In Patent Document 2, on the other hand, features indicating abnormality in a scanned image are highlighted and/or displayed comparably with the original scanned image as a user interface of a CAD for enhancing convenience of an operator. 
     Meanwhile, a technique has been developed for generating an image displaying the inside of a hollow organ developed about the axis of the hollow organ (hereinafter referred to as an panoramic image) as an image displaying method for actively diagnosing the insides of the hollow organs such as the large intestine (Patent Document 3). The panoramic images are advantageous in that doctors and the like can easily find lesion candidates because the entire surface of the hollow organ inside is viewable simultaneously. Further, a technique has been developed for generating a virtual endoscope image based on volume image data organized by the accumulation of plural sheets of tomographic images obtained by the aforementioned devices such as the X-ray CT devices (Patent Document 4). A virtual endoscope image is displayed by a method of: irradiating a projection object with a virtual ray from a virtual point-of-view set in the inside of a hollow organ; extracting a voxel with a brightness value greater than or equal to a predetermined threshold from voxels arranged on a line-of-sight; and projecting the extracted voxel on a projection surface. Similarly to an image obtained by an endoscope, the inside of an organ is observable with the virtual endoscope image (Patent Document 4). 
     PRIOR ART DOCUMENT 
     Patent Document 
     Patent Document 1: JP-A-2006-230910 
     Patent Document 2: JP-A-2008-512161 
     Patent Document 3: Japanese Patent No. 3627066 
     Patent Document 4: JP-A-7-296184 
     SUMMARY OF THE INVENTION 
     Problems that the Invention is to Solve 
     However, non-lesions such as the folds on the surface of an organ and/or lesions whose dimensions (e.g., polyp diameters) are extremely small are extracted when lesion candidate regions are extracted based on a curvature value as seen in the conventional CADs. Consequently, a drawback has been produced that lesion candidates cannot be narrowed down. Further, dimensions (e.g., polyp diameters) of lesions, intended to be detected by the CADs, depend on diagnostic purposes such as early detection of lesions and detection of advanced lesions. Various algorithms have been developed for extracting lesion candidate regions in accordance with features or the like of lesion tissues and polyps. The algorithms are lack of versatility although specialized in their respective purposes. 
     The invention is produced in view of the aforementioned drawbacks. It is an object of the invention to provide an image processing device and the like for detecting lesion regions under a condition that an operator is allowed to easily change detection objects in accordance with diagnostic purposes. 
     Means for Solving the Problems 
     To achieve the aforementioned object, a first aspect of the invention relates to an image processing device for detecting a lesion candidate region from a medical image. The image processing device is characterized to include: parameter setting means for setting a parameter to be used for detecting the lesion candidate region; and lesion candidate region detection means for assessing the medical image using the parameter set by the parameter setting means and detecting the lesion candidate region based on a result of the assessment. 
     Further, the image processing device preferably includes a data table that values of the parameter are preliminarily set in accordance with modes. The parameter setting means preferably includes first inputting means for reading out a value of the parameter corresponding to a selected mode from the data table and inputting the read-out value. 
     Further, the image processing device preferably includes second inputting means configured to input a numerical value as a value of the parameter. The parameter setting means preferably sets the numerical value input by the second inputting means as a value of the parameter. 
     The image processing device preferably includes third inputting means for displaying an object that a size or shape thereof varies in accordance with magnitude of a value of the parameter on a display screen displaying the medical image and inputting a value of the parameter through an operation with respect to the object. The parameter setting means preferably sets a value input by the third inputting means in accordance with the size or shape of the object as a value of the parameter. 
     The parameter setting means includes parameter inputting means configured to input a first parameter, and second parameter calculating means configured to calculate a second parameter based on the first parameter input by the parameter inputting means. The lesion candidate detecting means includes lesion candidate region extracting means and false positive deleting means. The lesion candidate region extracting means calculates a feature amount indicating the shape of an organ surface using the second parameter calculated by the second parameter calculating means with respect to the medical image and extracts the lesion candidate region based on the calculated feature amount. The false-positive deleting means determines a false-positive region by assessing a predetermined feature amount of the lesion candidate region extracted by the lesion candidate region extracting means and deletes the lesion candidate region when the lesion candidate region is determined as the false-positive region. 
     The second parameter is an inter-distance between differentiation reference points to be used in calculating a curvature value as the feature amount indicating the shape of the organ surface. 
     Further, the parameter setting means includes: parameter inputting means configured to input a first parameter; and third parameter calculating means configured to calculate a third parameter based on the first parameter input by the parameter inputting means. The lesion candidate detecting means includes lesion candidate region extracting means and false-positive deleting means. The lesion candidate region extracting means calculates a feature amount indicating the shape of the organ surface with respect to the medical image and extracts the lesion candidate region based on the calculated feature amount. The false-positive deleting means determines a false-positive region by assessing a predetermined feature amount of the lesion candidate region extracted by the lesion candidate region extracting means using the third parameter calculated by the third parameter calculating means and deletes the lesion candidate region when the lesion candidate region is determined as the false-positive region. 
     The third parameter includes at least either a parameter indicating the size of the lesion candidate region or a parameter indicating the shape of the lesion candidate region. 
     Further, the image processing device preferably further includes parameter correcting means for correcting the parameter set by the parameter setting means in accordance with deformation of the medical image. The lesion candidate region detecting means preferably assesses the medical image using the parameter corrected by the parameter correcting means and detects the lesion candidate region based on a result of the assessment. 
     The medical image is preferably an panoramic image displaying the inner surface of a hollow organ developed about an axis of the hollow organ. 
     The medical image is preferably a virtual endoscope image obtained by projecting the inside of a hollow organ on a predetermined projection plane from a virtual point-of-view set in the inside of the hollow organ. 
     A second aspect of the invention relates to an image processing method of detecting a lesion candidate region from a medical image. The image processing method is characterized to include: a parameter setting step of setting a parameter to be used for detecting the lesion candidate region; and a lesion candidate region detecting step of assessing the medical image using the parameter set in the parameter setting step and detecting the lesion candidate region based on a result of the assessment. 
     Advantage of the Invention 
     According to the invention, it is possible to provide an image processing device and the like for detecting lesion regions under a condition that an operator is allowed to easily change detection objects in accordance with diagnostic purposes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a hardware configuration diagram illustrating the entire configuration of an image processing system  1 . 
         FIG. 2  is a flowchart representing the entire image processing flow to be executed in the image processing system  1 . 
         FIG. 3  is a flowchart explaining the flow of a processing regarding lesion candidate detection to be executed by a medical image processing device  100 . 
         FIG. 4  is a data configuration diagram of a main memory  102  (first exemplary embodiment). 
         FIG. 5  is an exemplary data table 2 that values of a parameter P 1  are preliminarily set in accordance with modes. 
         FIG. 6  illustrates a display example of an panoramic image  71  and a parameter setting window  72 . 
         FIG. 7  is a diagram explaining the shape index. 
         FIG. 8  is a diagram explaining inter-distance between differentiation reference points. 
         FIG. 9  is a diagram explaining directions of an panoramic image and directions of a pixel on the panoramic image. 
         FIG. 10  is a flowchart explaining the flow of a pixel deformation calculation processing. 
         FIG. 11  is a data configuration diagram of the main memory  102  (second exemplary embodiment). 
         FIG. 12  is a diagram explaining a pathway radius. 
         FIG. 13  is a diagram explaining distance between adjacent cross-sections. 
         FIG. 14  is a diagram explaining the relation between a position on a hollow surface and a pixel size in the longitudinal direction. 
         FIG. 15  is a diagram illustrating a display example of lesion candidate regions to be obtained in executing a lesion candidate detection processing based on a deformation adjusted parameter obtained by correcting pixel deformation. 
         FIG. 16  is a diagram explaining cross-sectional correction in a sharply curved region. 
         FIG. 17  is a diagram explaining the positional relation among points in calculating pixel deformation in a third exemplary embodiment. 
         FIG. 18  is a flowchart explaining the flow of a pixel deformation calculation processing in the third exemplary embodiment. 
         FIG. 19  is a data configuration diagram of the main memory  102  (third exemplary embodiment). 
         FIG. 20  is a diagram explaining a virtual endoscope image. 
         FIG. 21  is a diagram explaining deformation due to distance from a point-of-view to a projected object. 
         FIG. 22  is a diagram explaining deformation to be produced in end portions of the virtual endoscope image. 
         FIG. 23  is a diagram explaining perspective projection. 
         FIG. 24  is a flowchart representing the flow of a processing of calculating inter-distance between differentiation reference points in the virtual endoscope image. 
         FIG. 25  is a data configuration diagram of the main memory  102  (fourth exemplary embodiment). 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     Preferred exemplary embodiments of the invention will be hereinafter explained in detail with reference to attached figures. 
     (First Exemplary Embodiment) 
     First, the following explanation relates to the configuration of an image processing system  1  that an image processing device of the invention is applied. 
       FIG. 1  is a hardware configuration diagram illustrating the entire configuration of the image processing system  1 . 
     As illustrated in  FIG. 1 , the image processing system  1  includes a display device  107 , a medical image processing device  100  equipped with an input device  109 , and an image database  111  connected to the medical image processing device  100  through a network  110 . 
     The medical image processing device  100  is a computer installed in a hospital or the like for image diagnostic purposes, and functions as a computer-aided detection (CAD) for analyzing a medical image, detecting lesion candidates from shadows in the medical image, and providing a doctor with the detected lesion candidates. 
     The medical image processing device  100  includes CPU (central processing unit)  101 , a main memory  102 , a storage device  103 , a communication interface (communication I/F)  104 , a display memory  105 , and an interface (I/F)  106  with external devices such as a mouse  108 . These components are respectively connected through a bus  112 . 
     CPU  101  loads a program stored in the main memory  102 , the storage device  103  or the like into a working memory area in RAM of the main memory  102 , and executes the loaded program. Accordingly, drive controls are executed for the components connected through the bus  112  and the medical image processing device  100  is thereby allowed to implement various processing. 
     In the first exemplary embodiment, CPU  101  further executes a processing regarding legion candidate detection described below (see  FIGS. 2 and 3 ). 
     The main memory  102  includes ROM (read only memory), RAM (random access memory) and the like. ROM permanently stores programs (e.g., a computer boot program and BIOS), data and the like. On the other hand, RAM temporarily stores programs, data and the like loaded from ROM, the storage device  103  and the like, and includes a working area to be used for allowing CPU  101  to execute various processing. 
     The storage device  103  is a storage device for reading/writing data from/to HDD (hard disc drive) and other storage media, and stores programs executed by CPU  101 , data necessary for executing the programs, OS (operating system) and the like. A control program corresponding to OS and application programs are stored as the aforementioned programs. CPU  101  reads out respective program codes, transfers them to RAM of the main memory  102 , and executes them as various means on an as-needed basis. 
     The communication I/F  104  includes a transmission control device, communication ports and the like, and mediates communication between the medical image processing device  100  and the network  110 . Further, the communication I/F  104  controls the communication through the network  110  with the image database  111  and other computers or machines such as the X-ray CT machines or the MRI machines. 
     I/F  106  is a port for connecting peripheral devices to the medical image processing device  100 , and data is transmitted/received to/from the peripheral devices through I/F  106 . For example, an input device (e.g., the mouse  108 ) and the like may be connected to the medical image processing device  100  through I/F  106 . 
     The display memory  105  is a buffer for temporarily storing display data to be input therein from CPU  101 . The stored display data is output to the display device  107  at a predetermined timing. 
     The display device  107  includes a display unit (e.g., a liquid crystal panel or a CRT monitor) and a logic circuit for executing a display processing in cooperation with the display unit. The display device  107  is connected to CPU  101  through the display memory  105 . The display device  107  displays the display data stored in the display memory  105  on the display unit in response to the control by CPU  101 . 
     The input device  109  is, for instance, an input device such as a keyboard, and outputs to CPU  101  various commands and information to be input by an operator. An operator interactively operates the medical image processing device  100  using the external devices including the display device  107 , the input device  109 , the mouse  108  and the like. 
     The network  110  includes various communication networks such as LAN (local area network), WAN (wide area network), Intranet and Internet, and mediates the communication connections between the medical image processing device  100  and other components including the image database  111 , a server, other information devices and the like. 
     The image database  111  accumulates and stores medical images taken by machines (the X-ray CT machines, the MRI machines, etc.) for taking images to be used for medical diagnostic purposes. For example, the image database  111  is installed in a server or the like of a hospital, a medical center or the like. In the image processing system  1  illustrated in  FIG. 1 , the image database  111  is designed to be connected to the medical image processing device  100  through the network  110 . However, the image database  111  may be installed in the storage device  103 , for instance, within the medical image processing device  100 . 
     It is noted that the medical images, handled in the image processing system  1  of the invention, include tomographic images of subjects, panoramic images of the hollow organs, and virtual endoscope images. An panoramic image displays the inside of a hollow organ developed about the axis (pathway line) of the hollow organ (see  FIG. 6 ). A virtual endoscope image displays the inside of a hollow organ from a virtual point-of-view set in the inside of the hollow organ with a display method based on perspective projection (see  FIG. 20(   b )). 
     Detection of lesion candidates in an panoramic image will be explained in the following first to third exemplary embodiments, whereas detection of lesion candidates in a virtual endoscope image will be explained in the following fourth exemplary embodiment. 
     Next, actions of the image processing system  1  will be explained with reference to  FIGS. 2 to 8 . 
       FIG. 2  is a flowchart representing the entire flow of the image processing to be executed in the image processing system  1 . 
       FIG. 3  is a flowchart explaining the flow of the processing regarding lesion candidate detection to be executed by the medical image processing device  100 . 
       FIG. 4  is a diagram representing data to be stored in RAM of the main memory  102  in executing the image processing and the lesion candidate detection processing. 
       FIG. 5  is a diagram representing an exemplary data table 2 that values of a parameter P 1  are set in accordance with modes of the present exemplary embodiment. 
       FIG. 6  is a display example of an panoramic image  71  and a parameter setting window  72 . 
       FIG. 7  is a diagram explaining the shape index. 
       FIG. 8  is a diagram explaining inter-distance between differentiation reference points. 
     CPU  101  of the medical image processing device  100  reads out programs and data related to an image processing and a lesion candidate detection processing from the main memory  102  and executes the image processing and the lesion candidate detection processing based on the read-out programs and data. 
     At the onset of executing the image processing to be described, it is assumed that the image data has been already downloaded from the image database  111  or the like through the network  110  and the communication I/F  104  and stored in the storage device  103  of the medical image processing device  100 . 
     In the image processing of  FIG. 2 , CPU  101  of the medical image processing device  100  firstly executes a processing of downloading image data. CPU  101  causes the display device  107  to display an image selection window displaying plural images to be selected in a list format or a thumbnail format, and receive a selection of an image from an operator. When an operator selects an intended image, CPU  101  reads out the corresponding image data of the selected image from the storage device  103  and keeps in the main memory  102  (Step S 101 ,  102   a  in  FIG. 4 ). 
     In the present exemplary embodiment, the image data of a hollow region is assumed to be selected. Further, the image data  102   a  to be loaded in this phase is assumed to be volume image data organized by the accumulation of plural tomographic images. 
     Next, CPU  101  creates a display image from the image data  102   a  loaded in Step S 101 . An panoramic image is herein assumed to be created as the display image. CPU  101  obtains hollow wall coordinate data  102   b  from the image data  102   a . The hollow wall coordinate data  102   b  includes a real-space coordinate (x, y) corresponding to each point (each pixel) on a hollow wall displayed as the panoramic image, and a distance f (x, y) from a point on the hollow surface corresponding to the coordinate to a line passing through roughly the hollow center (hereinafter referred to as a pathway line) in the three-dimensional coordinate. The distance f (x, y) is referred to as “depth data” that is created by CPU  101  in creating an panoramic image. CPU  101  keeps the obtained hollow wall coordinate data  102   b  in the main memory  102  (Step S 102 ,  102   b  in  FIG. 4 ). 
     It is noted that creation of an panoramic image is disclosed in the aforementioned Patent Document 3 (Publication of Japan Patent No. 3627066) and explanation thereof will be hereinafter omitted. 
     Next, CPU  101  detects lesion candidates based on the hollow wall coordinate data  102   b  obtained in Step S 102  (Step S 103 ; continued to the lesion candidate detection processing in  FIG. 3 ). 
     In the lesion candidate detection processing of  FIG. 3 , CPU  101  firstly sets a parameter to be used for the processing of detecting lesion candidates (Step S 201 ). The parameter, set in Step S 201 , is referred to as “the parameter P 1 ”. 
     Values indicating dimension such as length (polyp diameter), area, volume and the like of a lesion can be assumed as the parameter P 1 . In the present exemplary embodiment, a value indicating the length (polyp diameter) of a lesion (e.g., polyp) as a detection object is set as the parameter P 1 , for instance. 
     The parameter P 1  is also used for calculating a parameter P 2  (inter-distance between differentiation reference points) to be used for calculating a curvature in Step S 202 , a parameter P 3  (a threshold of a region diameter) to be used for a false-positive deleting processing in Step S 204 , and a parameter P 4  (a threshold of a degree of circularity). 
     The aforementioned parameter P 2 , representing % inter-distance between differentiation reference points, is obtained by the following equation (1).
 
 P 2 =A×P 1  (1)
 
     Further, the aforementioned parameter P 3 , representing a threshold of a diameter (region diameter) of an extracted lesion candidate region, is expressed by the following equation (2).
 
 P 3 =B×P 1  (2)
 
     Yet further, the aforementioned parameter P 4 , representing a threshold of a degree of circularity of an extracted lesion candidate region, is expressed by the following equation (3).
 
 P 4 =C/P 1  (3)
 
     In the above equations, A, B and C are constants. 
     In the parameter setting of Step S 201 , CPU  101  may be configured to read out one of the values preliminarily set in accordance with modes from the data table 2 of  FIG. 5 . Alternatively, an operator may be allowed to input a given numerical value through the input device  109 . 
     Further alternatively, an object (e.g., a polyp image) may be displayed on the panoramic image created in the aforementioned Step S 102 , and the magnitude of the parameter P 1  may be input by manipulating the size or shape of the object through an input operation using a pointing device (e.g., the mouse  108 ) and/or the input device  109 . In this case, CPU  101  sets a value corresponding to the size (diameter) or shape expressed by the object as the parameter P 1  and keeps the set value in the parameter P 1  ( 102   c  in  FIG. 4 ) of the main memory. 
     In the data table 2 represented in  FIG. 5 , for example, different default values have been preliminarily set for the respective modes as follows: “6” for an “early detection” mode; “10” for a “normal” mode; and “8” for a “manual” mode. In the data table 2, “display on/off” represents “on/off” of a mode change switch where “1” represents an “on” state and “0” represents an “off” state. 
       FIG. 6  illustrates a situation that the panoramic image  71  is displayed on the upper part of the display screen of the display device  107  while the parameter setting window  72  is displayed on the lower part of the display screen. In actual situations, shadows of the organ surface are displayed by grayscale (concentration information) in the panoramic image  71 . In  FIG. 6 , however, the organ surface is expressed with solid lines for clearly expressing the drawing. Simply put, a region vertically interposed between two lines  711  corresponds to the inside surface of a hollow organ whereas plural vertical lines  712  depicted within the region correspond to folds of the organ surface. 
     In the parameter setting window  72  of  FIG. 6 , a mode list  721  of the selectable modes is displayed together with radio buttons, and a numerical value input box  722  for the parameter P 1  is further displayed. Further, a “display” button  723  is pressed using the mouse  108  or the like after the parameter P 1  is set. When an operator presses the “display” button  723  using the mouse  108  or the like, CPU  101  executes a lesion candidate detection processing (Steps S 202  to S 204 ) and lesion candidate regions are accordingly distinguishably displayed on the panoramic image  71 . 
     In the example of  FIG. 6 , the “early detection” mode is being selected in the mode list  721  and “10” mm is further assumed to be input in the numerical value input box  722 . When an operator inputs a value in the numerical value input box  722 , the input value may be preferentially used even if a value of “6”, preliminarily set for the “early detection” mode in the data table 2, is thus input as a default value. Subsequently, lesion candidates having a polyp diameter (the parameter P 1 ) of roughly “10” mm are detected in the processing of Step S 202  and thereafter to be described in response to the press of the display button  723 , and the detected lesion candidates are accordingly distinguishably displayed as depicted with a marking  713 . 
     After setting the parameter P 1  as described above, CPU  101  calculates the parameters P 2 , P 3  and P 4  based on the parameter P 1  using the aforementioned equations (1), (2) and (3) and keeps the calculated parameters in the main memory  102  ( 102   d ,  102   e  and  102   f  in  FIG. 4 ). 
     When the parameters P 1 , P 2 , P 3  and P 4  are set in Step S 201 , CPU  101  calculates a first feature amount for each pixel p in the panoramic image  71  using the depth data f (x, y) of the panoramic image  71  ( 102   b  in  FIG. 4 ). For example, the first feature amount is set as a curvature value. The curvature value is typified by the shape index, for instance (Step S 202 ). CPU  101  keeps the calculated curvature value in the main memory  102  ( 102   g  in  FIG. 4 ). 
     As represented in  FIG. 7 , the shape index is expressed by continuously varying values ranging from 0 to 1. The respective values correspond to different curved surface states. Simply put, a concave hemisphere corresponds to a shape index value of “0”. A concave half-column, a saddle-shaped surface and a plane; a convex half-column, and a convex hemisphere are sequentially expressed in proportion to increase in a shape index value from “0”. The convex hemisphere corresponds to a shape index value of “1”. 
     The shape index is calculated by the following equation (4). 
     
       
         
           
             
               
                 
                   
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     In the above equation, λ max  is a maximum value of a principle curvature for each point on a curved surface, whereas λ min  is a minimum value of the principle curvature for each point on the curved surface. 
     The maximum value λ max  and the minimum value λ min  of the principle curvature are calculated by the following equations (5). 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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     In the above equations, f xx , f yy  and f xy  are second-order partial derivatives of f (x, y) in an intended pixel p and are calculated by the following equations (6) using a coordinate (x, y) of the intended pixel p and the depth data f (x, y) in the intended pixel p. 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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                   } 
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     In the above equations, P 2  is the inter-distance between differentiation reference points calculated in the aforementioned equation (1). The inter-distance between differentiation reference points refers to the distance between the intended pixel p and a pixel to be referred in calculating the second-order partial derivatives of the equations (6). 
       FIG. 8  is a diagram for explaining the inter-distance between differentiation reference points. 
     As an example, inter-distance P 2  between differentiation reference points is herein set to be ½ of the parameter P 1  (polyp diameter). Simply put, A is set to be ½ (i.e., A=½) in the aforementioned equation (1). 
     In calculating a curvature of a convex surface  601  illustrated in  FIG. 8 , a curvature value depends on the inter-distance between differentiation reference points. The curvature value is maximized when the inter-distance between differentiation reference points is roughly the same as the width of the curved surface (convex/concave). As depicted with arrows  602  in  FIG. 8 , a curvature is calculated for a substantially flat surface when the inter-distance between differentiation reference points is less than the width of the convex/concave. A shape index value of roughly 0.5 is herein obtained. On the other hand, a slope of the convex surface can be obtained in calculating the second-order partial derivatives when the inter-distance between differentiation reference points is roughly the same as the width of the convex/concave as depicted with arrows  603  in  FIG. 8 . Accordingly, a shape index value of roughly 1 is herein obtained, and this indicates that the convex surface is formed in an approximately convex hemispheric shape. 
     In the aforementioned example, ½ is set as the constant A (Equation (1)) to be used for calculating the inter-distance between differentiation reference points. However, the value of the constant A is not necessarily limited to the above. 
     Next, CPU  101  executes a threshold processing based on the calculated shape index (curvature value  102   g ) for each pixel p and extracts lesion candidate regions (Step S 203 ). CPU  101  keeps the extracted lesion candidate regions in the main memory (a lesion candidate region  102   h  in  FIG. 4 ). 
     The lesions (polyps) are formed in the shape of a convex curved surface. It is therefore herein assumed to preliminarily set the lower limit to the shape index, and CPU  101  determines a given pixel as a lesion candidate region when the pixel has a curvature value greater than or equal to the lower limit. For example, the lower limit is set to be 0.5. 
     Smaller curvature values are herein calculated in the case of a convex curved surface sized with a width extremely greater than the inter-distance between differentiation reference points. Therefore, such curvature values are exempted from lesion candidates and are not thereby extracted. 
     Next, CPU  101  calculates second and third feature amounts for each of the extracted lesion candidate regions (intended regions) and keeps the calculated feature amounts in the main memory  102  (a feature amount (region diameter)  102   i  and a feature amount (degree of circularity)  102   j  in  FIG. 4 ). It is herein assumed that the second feature amount is a region diameter d of a lesion candidate region whereas the third feature amount is a degree-of-circularity k of a lesion candidate region. Next, CPU  101  executes the following assessment with respect to the second and third feature amounts of the respective intended regions. When a given intended region is determined to be a false-positive as a result of the determination, the intended region is deleted from the lesion candidate regions listed in Step S 203  (Step S 204 ). 
     When the region diameter d as the second feature amount is assessed in the false-positive deleting processing of Step S 204 , CPU  101  calculates the region diameter d of each lesion candidate region with reference to the coordinate data of each point on the hollow surface in the three dimensional real space. CPU  101  compares the calculated region diameter d ( 102   i  in  FIG. 4 ) with the parameter P 3  (equation (2);  102   e  in  FIG. 4 ) set in Step S 201 . When the relation “d&lt;P 3 ” is established, CPU  101  determines that the intended region is a false-positive and deletes the intended region from the lesion candidate region  102   h.    
     When the degree-of-circularity k as the third feature amount is assessed in the false-positive deleting processing of Step S 204 , CPU  101  calculates the degree-of-circularity k of each lesion candidate region with reference to the coordinate data of each point on the hollow surface in the three dimensional real space. CPU  101  compares the calculated degree-of-circularity k ( 102   j  in  FIG. 4 ) with the parameter P 4  (equation (3);  102   f  in  FIG. 4 ) set in Step S 201 . When the relation “k&lt;P 4 ” is established, CPU  101  determines that the intended region is a false-positive (a false-positive region  102   k  in  FIG. 4 ) and deletes the false-positive region  102   k  from the lesion candidate region  102   h.    
     The region diameter d and the degree-of-circularity k are assessed as the feature amounts in the false-positive deleting processing of Step S 204 . However, the feature amounts are not limited to the above. For example, a horizontal-vertical ratio, a CT value and the like of an intended region may be set as the feature amounts, and a false-positive region may be determined based on the feature amounts. 
     Alternatively, the curvedness may be used as a feature amount to be used in the false-positive deleting processing of Step S 204  and the like. The curvedness indicates the size of the curved surface. As to the convex surfaces, a large value of the curvedness indicates a small convex surface whereas a small value of the curvedness indicates a large convex surface. Therefore, the curvedness can be used as an indicator of a polyp diameter to be assessed. The curvedness is expressed by the following equation (7). 
     
       
         
           
             
               
                 
                   curvedness 
                   = 
                   
                     
                       
                         
                           λ 
                           max 
                           2 
                         
                         + 
                         
                           λ 
                           min 
                           2 
                         
                       
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     When the curvedness is set as a feature amount, it is determined whether or not a given intended lesion candidate region is a false-positive by comparing average of the curvedness of the entire intended lesion candidate region with a predetermined value (a value proportional to inverse of the parameter P 1 ). Further, an AND condition determination may be executed for the shape index and the curvedness in the threshold processing of Step S 203 . 
     The processing proceeds to Step S 104  in  FIG. 2  when the false-positive deleting processing of Step S 204  is completed and a false-positive is accordingly deleted. CPU  101  distinguishably displays the lesion candidate regions on the panoramic image  71  using the markings  713  and the like (Step S 104 ; see  FIG. 6 ) and completes the image processing. 
     As explained above, the medical image processing device  100  in the image processing system  1  of the first exemplary embodiment executes a processing of detecting lesion candidate regions from an image of the organ surface. In the lesion candidate detection processing, the parameters to be used for detecting the lesion candidate regions are set in accordance with modes. Alternatively, an operator is allowed to set the parameter through the manual input of numerical values or through GUI. In the present exemplary embodiment, four types of parameters P 1 , P 2 , P 3  and P 4  are used, and the following settings are established: P 1  as a polyp diameter; P 2  as inter-distance between differentiation reference points; P 3  as a threshold of a region diameter; and P 4  as a threshold of a degree of circularity. CPU  101  calculates a shape-related feature amount (curvature value) of each point on the organ surface using a set parameter (P 2 ) and determines a given point as a lesion candidate region when it corresponds to a predetermined shape. 
     Further, CPU  101  calculates the feature amounts such as a region diameter and a degree of circularity for the detected lesion candidate regions, determines whether or not the feature amounts correspond to the lesion candidates using the parameters (P 3 , P 4 ), and deletes a given lesion candidate region when it is determined as a false-positive. Subsequently, CPU  101  distinguishably displays the lesion candidate regions on the image excluding the false-positive regions. 
     Thus, the parameters related to the detection of the lesion candidate regions are set in accordance with modes or are set by an operator. This makes it possible to detect lesion candidate regions under the condition that targets are easily changed in accordance with diagnostic purposes and to enhance versatility of CAD. 
     Further in the image processing system  1  of the present exemplary embodiment, the parameter to be used for calculating a curvature value (the inter-distance P 2  between differentiation reference points) and the parameter to be used for assessing the feature amount in the false-positive deleting processing (the region diameter P 3  and the degree-of-circularity P 4 ) are calculated from the parameter (P 1 ) firstly set by an operator. In other words, the single parameter P 1  is secondarily used. 
     Thus, the parameters to be used for determining the other feature amounts (P 2 , P 3  and P 4 ) are calculated from the single set parameter (P 1 ). Therefore, it is not necessary to separately input many parameters and this reduces complexity and effort of the parameter setting. Further, an operator is allowed to intuitively operate CAD easily when the parameter to be set by the operator is of a highly visible type indicating the size or shape of lesions (e.g., a polyp diameter). Yet further, operability will be enhanced when GUI is used for inputting the parameter. 
     In the present exemplary embodiment, the parameters P 1  and P 2  are set in association with each other where the parameter P 1  is set as the length of a lesion and the parameter P 2  is set as inter-distance between differentiation reference points. Accordingly, the lesion candidate region extracting processing can be executed using an appropriate value of the inter-distance between differentiation reference points in accordance with the length of a lesion candidate intended to be extracted, and it is possible to prevent a non-targeted lesion candidate from being extracted. 
     It is herein noted that the medical image exemplified in the first exemplary embodiment is an panoramic image of a hollow organ. However, the medical image is not limited to the above, and various medical images may be used including tomographic images, three dimensional volume images or the like of a subject. In such cases, the medical image processing device  100  is allowed to set the parameters related to the lesion candidate detection, and detects lesion candidates using the set parameters. 
     Further, the parameter of P 1 , amongst the parameters, is only configured to be input. However, parameters to be input are not limited to the parameter P 1 . For example, values preliminarily set in accordance with modes and operator&#39;s desired values may be input in the other parameters (P 2 , P 3  and P 4 ). 
     (Second Exemplary Embodiment) 
     Next, the image processing system  1  of a second exemplary embodiment will be explained. The hardware configuration of the image processing system  1  of the second exemplary embodiment is the same as the image processing system  1  of the first exemplary embodiment of  FIG. 1 . Therefore, explanation thereof will be hereinafter omitted, and a given component shared between the first and second exemplary embodiments will be explained while being given the same reference numeral. 
     In general, image deformation occurs in an panoramic image. It is therefore necessary to execute a processing in consideration of the image deformation for more accurately assessing the shape of the organ surface in detecting lesion candidates. 
     In the second exemplary embodiment, parameter correction is executed based on deformation of an panoramic image in setting parameters to be used for detecting lesion candidates. 
     The reasons of deformation of an panoramic image are assumed to be variation in pixel sizes (dy in  FIG. 9 ), along a direction (y direction; hereinafter referred to as “a lateral direction”) perpendicular to a longitudinal direction of the hollow organ with respect to each longitudinal (x directional) position of the panoramic image, curved of a hollow organ and the like. In other words, the circumference of a cross-section of a hollow organ in a given x position (longitudinal position) is assigned to pixels in the lateral direction at predetermined angular intervals in generating an panoramic image. However, circumferences of longitudinal cross-sections of an actual hollow organ are different from each other. Therefore, the pixel size dy varies and this results in deformation of an panoramic image. 
     Further, the distance between adjacent x positions (dx in  FIG. 9 ) on the inside of a given curved region of a hollow organ is different from that on the outside of the curved region of the hollow organ. This results in image deformation. 
     In view of the above, in the second exemplary embodiment, image deformation is calculated for each pixel by a pixel deformation calculation processing represented in  FIG. 10  and calculates a deformation adjusted parameter (P 2 _x, P 2 _y) obtained by correcting the parameter P 2  based on the calculated pixel deformation when the parameter setting processing of Step S 201  is executed in the lesion candidate detection processing (see  FIG. 3 ) of the first exemplary embodiment. 
       FIG. 9  is a diagram for explaining the panoramic image  71  of a hollow organ and the directions of a given pixel on the panoramic image  71 . 
     In the panoramic image  71  of  FIG. 9 , a longitudinal pathway line of the hollow organ is set as an x direction whereas a direction perpendicular to the pathway line (lateral direction) is set as a y direction. Further, the length of the actual organ surface, corresponding to an edge of a pixel  715 , is referred to as the pixel size. The x-directional pixel size will be hereinafter expressed as “dx” whereas the y-directional pixel size will be expressed as “dy”. The pixel deformation is calculated as a ratio (dx/dy) between the x-directional pixel size and the y-direction pixel size in an intended pixel. 
       FIG. 10  is a flowchart explaining the flow of a pixel deformation calculation processing. 
       FIG. 11  is a diagram representing the data to be kept in RAM of the main memory  102  during execution of the pixel deformation calculation processing. 
       FIG. 12  is a diagram explaining a pathway radius R. 
       FIG. 13  is a diagram explaining the distance between an intended cross-section (hollow plane S n ) and its adjacent cross-section (hollow plane S n+1 ). 
       FIG. 14  is a diagram explaining the relation between positions on the hollow surface and the longitudinal pixel sizes. 
     CPU  101  of the medical image processing device  100  of the second exemplary embodiment reads out the programs and data related to the pixel deformation calculation processing represented in  FIG. 10  from the main memory  102  and executes the pixel deformation calculation processing based on the read-out programs and data. 
     It is herein assumed that the image data is downloaded from the image database  111  or the like through the network  110  and is stored in the storage device  103  of the medical image processing device  100  on the onset of executing the following processing. 
     In the pixel deformation calculation processing, CPU  101  of the medical image processing device  100  firstly loads panoramic image data  102   l , three dimensional space coordinate data  102   m  that contains the coordinates of points in the three dimensional space corresponding to points on the panoramic image, and coordinate data  102   n  of points on the pathway line (hereinafter referred to as “pathway points”) from the storage device  103 , and keeps the loaded data in the main memory  102  (Step S 301 ;  102   l ,  102   m  and  102   n  in  FIG. 11 ). 
     The coordinate data  102   a  of the pathway points herein refers to the three dimensional real space coordinate data of points in each of which the pathway line intersects at right angle with a given cross-section (hereinafter referred to as “an intended hollow plane S n ”) having a row of pixels aligned in the lateral direction on the panoramic image  71  as the hollow surface. The pathway point will be set as a pathway point on the intended hollow plane S n  and is referred to as an intended pathway point Q n  (see  FIG. 12 ). 
     Next, CPU  101  sequentially scans the respective pixels on the panoramic image  71  and calculates the image deformation (dx/dy) for each point (pixel). The flowchart in  FIG. 10  exemplifies a case that the pixels on the panoramic image  71  are firstly scanned along the lateral direction and then scanned along the longitudinal direction. 
     Pixel deformation occurred in the panoramic image  71  varies in accordance with how the pathway line is curved, i.e., magnitude of curve of the hollow organ. Therefore, CPU  101  firstly determines magnitude of curve. 
     CPU  101  calculates the pathway diameters R with respect to a curved region of the hollow organ using the coordinate data  102   n  of the pathway points and keeps the calculated pathway diameters R in the main memory  102  (Step S 302 ,  102   o  in  FIG. 11 ). The pathway diameter R will be herein explained with reference to  FIG. 12 .  FIG. 12  illustrates a curved region of a hollow organ  8 . A pathway line  82  is a line passing through roughly the center of the hollow organ  8 . The n-th point on the pathway line  82  is expressed as Q n . The pathway diameter R is a radius R of a circle passing through three points of: the intended pathway point Q n ; and pathway points Q n−N  and Q n+N  that are both separated from the intended pathway point Q n  at an interval of a predetermined number of points N. The origin of the circle, passing through the pathway points Q n , Q n−N  and Q n+N , is set as an origin O. A curved region has a gentle curve when the pathway diameter R is large, whereas a curved region has a sharp curve when the pathway diameter R is small. 
     CPU  101  determines how the hollow organ  82  is curved based on the calculated pathway diameter R (Step S 303 ). For example, it is determined that the intended pathway point Q n  is disposed on a gentle curve when the value of the pathway diameter R is greater than or equal to a predetermined threshold Rt. On the other hand, it is determined that the intended pathway point Q n  is disposed on a sharp curve when the value of the pathway diameter R is less than the predetermined threshold Rt. 
     When it is determined that the intended pathway point Q n  is disposed on a gentle curve (Step S 303 ; gentle curve), CPU  101  deals with lateral-directional pixel deformation without dealing with the deformation due to curve because curve hardly contributes to pixel deformation. 
     In Step S 304 , the x-directional pixel size dx is firstly calculated for the intended pixel p and the calculated x-directional pixel size dx is kept on the main memory  102  ( 102   p  in  FIG. 11 ). The pixel size dx is expressed by the following equation (8).
 
 dx =distance from pathway point  Q   n  to adjacent pathway point  Q   n+1   (8)
 
     Next in Step S 305 , the lateral directional pixel size dy is calculated for the intended pixel p and the calculated lateral directional pixel size dy is kept in the main memory  102  ( 102   q  in  FIG. 11 ). The image size dy is expressed by the following equation (9).
 
 dy =circumference of intended hollow plane  S   n /matrix size in  y -direction of panoramic image  (9)
 
     CPU  101  calculates the pixel deformation dx/dy based on the pixel sizes dx and dy calculated in Steps S 304  and S 305  (Step S 306 ) and keeps the calculated pixel deformation dx/dy in the array ( 102   r  in  FIG. 11 ). 
     When it is determined that the intended pathway point Q n  is disposed on a sharp curve (Step S 303 ; sharp curve), on the other hand, pixel deformation in the pathway line direction (longitudinal direction) occurs in the panoramic image  71  due to influence of the curve. Pixel deformation level due to curve depends on whether the pixel is disposed on the inside of the curve of the pathway line or on the outside of the curve of the pathway line. 
     Pixel deformation due to curve will be explained with reference to  FIGS. 13 and 14 . As illustrated in  FIG. 13 , on the adjacent hollow planes S n  and S n+1  of the hollow organ  8 , a small distance is produced from a pixel disposed inwards of the curve on one of the hollow planes to a corresponding pixel disposed on the other (adjacent one) of the hollow planes, whereas a large distance is produced from a pixel disposed outwards of the curve on one of the hollow planes to a corresponding pixel disposed on the other (adjacent one) of the hollow planes. 
     In  FIG. 14 , the pathway line direction of the hollow plane S n  is expressed along a direction perpendicular to the sheet of  FIG. 14 , and a situation is illustrated therein that respective points B n  on the edge of the hollow region (hollow surface  83 ) on the hollow plane S n  are projected on a projection plane t 0 . In  FIG. 14 , a point Q n  is an intended pathway point and a point B n  is a given point on the hollow surface  83 , whereas a point O corresponds to the point O in  FIG. 12  (the origin of the circle fitted to the pathway line  82 ). 
     Simply put, as illustrated in  FIG. 14 , it is determined whether the intended pixel p on the hollow surface  83  of the hollow plane S n  is disposed inwards or outwards of the curve based on magnitude of an angle θ formed by a vector Q n B n  directed from the intended pathway point Q n  to a given point B n  on the hollow surface and a vector Q n O directed from the point Q n  to the point O. More specifically, it is determined whether the intended pixel p is disposed inwards or outwards of the curve based on a projected coordinate q of a point B n ′ obtained by projecting the vector Q n B n  on the vector Q n O about the point Q n . 
     Simply put, the distance between projection coordinates of the intended hollow plane S n  and the adjacent hollow plane S n+1  at corresponding angles corresponds to a pixel size dx on the panoramic image corresponding to the point B n . 
     First, CPU  101  calculates a distance l 0  between the intended pathway point Q n  and the adjacent pathway point Q n+i  and keeps the calculated distance in the main memory  102  ( 102   s  in  FIG. 11 ). The distance l 0  is calculated from the coordinate data  102   n  of the pathway points in the three dimensional real space loaded in Step S 301  (Step S 307 ). 
     Next, CPU  101  calculates an average diameter r of the cross-section (hollow plane S n ) perpendicular to the intended pathway point Q n . In other words, CPU  101  refers to the three dimensional real space coordinates of pixels with a longitudinal (x-directional) coordinate identical to that of an intended pixel on the panoramic image, and calculates distances from the intended pathway point Q n  to the pixels. Subsequently, CPU  101  calculates average of the calculated distances and sets the calculated average as the average hollow radius r (Step S 308 ). CPU  101  keeps the calculated average radius r in the main memory  102  ( 102   t  in  FIG. 11 ). It is herein noted that the distance (average radius r) can be also calculated from the depth data. 
     Next, CPU  101  calculates the longitudinal pixel size dx in the intended pixel p (Step S 309 ). 
     In Step S 309 , CPU  101  firstly calculates a projected coordinate q of the intended pixel p. The projected coordinate is calculated by the following equation (10).
 
q=rosθ  (10)
 
     A value of an angle θ is herein calculated based on the coordinate of the center of the pathway diameter R (coordinate of the point O), the coordinate on the pathway line (coordinate of the point Q n ) and the coordinate on the hollow surface (point B) ( 102   u  in  FIG. 11 ). 
     CPU  101  calculates a value of the pathway-line-directional pixel size dx in the projected coordinate q using the pathway diameter R and the average hollow radius r (Step S 309 ;  102   p  in  FIG. 11 ). When the intended hollow plane S n  and the adjacent hollow plane S n+1  are disposed close to each other, it is assumed that the distance between the two planes at corresponding angles is linearly proportional to the projected coordinate q=rcos θ. Therefore, dx is calculated by the following equation (11). 
     
       
         
           
             
               
                 
                   
                     d 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     x 
                   
                   = 
                   
                     
                       
                         
                           ( 
                           
                             R 
                             - 
                             q 
                           
                           ) 
                         
                         R 
                       
                       ⁢ 
                       
                         l 
                         0 
                       
                     
                     = 
                     
                       
                         
                           ( 
                           
                             R 
                             - 
                             
                               r 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               cos 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               θ 
                             
                           
                           ) 
                         
                         R 
                       
                       ⁢ 
                       
                         l 
                         0 
                       
                     
                   
                 
               
               
                 
                   ( 
                   11 
                   ) 
                 
               
             
           
         
       
     
     Next, CPU  101  calculates the y-directional pixel size dy in the intended pixel p (Step S 310 ;  102   q  in  FIG. 11 ). 
     Similarly to Step S 305 , the pixel size dy is calculated by the following equation (12).
 
 dy =circumference of intended hollow plane  S   n /matrix size in  y -direction of panoramic image  (12)
 
     CPU  101  calculates the pixel deformation dx/dy based on the pixel sizes dx and dy calculated in Steps S 309  and  310  (Step S 311 ) and stores the calculated pixel deformation dx/dy in the array (Step S 312 ;  102   r  in  FIG. 11 ). 
     CPU  101  completes the pixel deformation calculation processing when the pixel deformation dx/dy is calculated for all the pixels by repeating the processing of Steps S 302  to S 312  for each pixel on the panoramic image as described above. 
     The pixel deformation dx/dy for each pixel, calculated in the pixel deformation calculation processing, is referred in executing curvature calculation in Step S 202  of the lesion candidate region detection processing in  FIG. 3 . 
     For example, CPU  101  calculates the deformation adjusted parameters P 2 _x and P 2 _y for the parameter P 2  based on the following equations (13). 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           P2_x 
                           = 
                           
                             A 
                             × 
                             P 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             1 
                             × 
                             
                               
                                 ⅆ 
                                 x 
                               
                               / 
                               
                                 ⅆ 
                                 y 
                               
                             
                           
                         
                       
                     
                     
                       
                         
                           P2_y 
                           = 
                           
                             A 
                             × 
                             P 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             1 
                           
                         
                       
                     
                   
                   } 
                 
               
               
                 
                   ( 
                   13 
                   ) 
                 
               
             
           
         
       
     
     In the above equation, P 2 _x is inter-distance between differentiation reference points in the longitudinal direction, whereas P 2 _y is inter-distance between differentiation reference points in a direction perpendicular to the longitudinal direction. A curvature value is obtained by calculating the following equations (14) using the deformation adjusted parameters P 2 _x and P 2 _y and calculating the shape index using the aforementioned equations (4), (5) and (6). 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             f 
                             
                               x 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               x 
                             
                           
                           = 
                           
                             
                               
                                 f 
                                 ⁡ 
                                 
                                   ( 
                                   
                                     
                                       x 
                                       + 
                                       P2_x 
                                     
                                     , 
                                     y 
                                   
                                   ) 
                                 
                               
                               + 
                               
                                 f 
                                 ⁡ 
                                 
                                   ( 
                                   
                                     
                                       x 
                                       - 
                                       P2_x 
                                     
                                     , 
                                     y 
                                   
                                   ) 
                                 
                               
                               - 
                               
                                 2 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   f 
                                   ⁡ 
                                   
                                     ( 
                                     
                                       x 
                                       , 
                                       y 
                                     
                                     ) 
                                   
                                 
                               
                             
                             
                               
                                 ( 
                                 P2_x 
                                 ) 
                               
                               2 
                             
                           
                         
                       
                     
                     
                       
                         
                           
                             f 
                             
                               y 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               y 
                             
                           
                           = 
                           
                             
                               
                                 f 
                                 ⁡ 
                                 
                                   ( 
                                   
                                     x 
                                     , 
                                     
                                       y 
                                       + 
                                       P2_y 
                                     
                                   
                                   ) 
                                 
                               
                               + 
                               
                                 f 
                                 ⁡ 
                                 
                                   ( 
                                   
                                     x 
                                     , 
                                     
                                       y 
                                       - 
                                       P2_y 
                                     
                                   
                                   ) 
                                 
                               
                               - 
                               
                                 2 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 
                                   f 
                                   ⁡ 
                                   
                                     ( 
                                     
                                       x 
                                       , 
                                       y 
                                     
                                     ) 
                                   
                                 
                               
                             
                             
                               
                                 ( 
                                 P2_y 
                                 ) 
                               
                               2 
                             
                           
                         
                       
                     
                     
                       
                         
                           
                             f 
                             
                               x 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               y 
                             
                           
                           = 
                           
                             
                               
                                 
                                   
                                     
                                       f 
                                       ⁡ 
                                       
                                         ( 
                                         
                                           
                                             x 
                                             + 
                                             P2_x 
                                           
                                           , 
                                           
                                             y 
                                             + 
                                             P2_y 
                                           
                                         
                                         ) 
                                       
                                     
                                     - 
                                     
                                       f 
                                       ⁡ 
                                       
                                         ( 
                                         
                                           
                                             x 
                                             - 
                                             P2_x 
                                           
                                           , 
                                           
                                             y 
                                             + 
                                             P2_y 
                                           
                                         
                                         ) 
                                       
                                     
                                     - 
                                   
                                 
                               
                               
                                 
                                   
                                     
                                       f 
                                       ⁡ 
                                       
                                         ( 
                                         
                                           
                                             x 
                                             + 
                                             P2_x 
                                           
                                           , 
                                           
                                             y 
                                             - 
                                             P2_y 
                                           
                                         
                                         ) 
                                       
                                     
                                     + 
                                     
                                       f 
                                       ⁡ 
                                       
                                         ( 
                                         
                                           
                                             x 
                                             - 
                                             P2_x 
                                           
                                           , 
                                           
                                             y 
                                             - 
                                             P2_y 
                                           
                                         
                                         ) 
                                       
                                     
                                   
                                 
                               
                             
                             
                               P2_x 
                               · 
                               P2_y 
                             
                           
                         
                       
                     
                   
                   } 
                 
               
               
                 
                   ( 
                   14 
                   ) 
                 
               
             
           
         
       
     
     Thus, curvature calculation can be executed based on a length in the real space even for a deformed image such as an panoramic image by correcting the parameter P 2  using the pixel deformation dx/dy in executing the curvature calculation. 
     It is herein noted that the deformation adjusted parameters P 2 _x and P 2 _y can be also calculated by the following equations (15). 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           P2_x 
                           = 
                           
                             A 
                             × 
                             P 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             1 
                             × 
                             d 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             x 
                           
                         
                       
                     
                     
                       
                         
                           P2_y 
                           = 
                           
                             A 
                             × 
                             P 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             1 
                             × 
                             d 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             y 
                           
                         
                       
                     
                   
                   } 
                 
               
               
                 
                   ( 
                   15 
                   ) 
                 
               
             
           
         
       
     
       FIG. 15  is a diagram illustrating a display example of lesion candidate regions to be obtained when the lesion candidate detection processing is executed using the deformation adjusted parameters P 2 _x and P 2 _y. 
     As illustrated in  FIG. 15 , lesion candidate regions  713   a ,  713   b  and  713   c  are distinguishably displayed on the panoramic image  71 . 
     In  FIG. 15 , the marking  713   a  indicates a lesion candidate region in a less deformed region; the marking  713   b  indicates a transversely deformed lesion candidate region; and the marking  713   c  indicates a vertically deformed lesion candidate region. Simply put, even if being a circular polyp in the real space, a region transversely deformed by the influence of the curve of a hollow organ in the real space cannot be detected as a lesion candidate without executing a lesion candidate detection using the deformation adjusted parameters. However, the shape in the real space can be properly assessed by executing curvature calculation using the deformation adjusted parameters P 2 _x and P 2 _y as executed in the present exemplary embodiment. Similarly, the shape in the real space can be properly assessed for a vertically deformed region such as the marking  713   c.    
     As described above, in the image processing system  1  of the second exemplary embodiment, CPU  101  calculates the deformation amount (dx/dy) of each pixel in both of the longitudinal and lateral directions by executing the pixel deformation calculation processing, corrects the parameter using the calculated deformation amount, and calculates the deformation adjusted parameters (P 2 _x, P 2 _y) in executing the lesion candidate detection processing for an panoramic image. Subsequently, CPU  101  executes curvature calculation and the like using the deformation adjusted parameters and detects lesion candidate regions. 
     Therefore, it is possible to properly assess the shape of the organ surface in the real space for even a longitudinally and/or laterally deformed image such as an panoramic image. Accordingly, detection accuracy will be enhanced for lesion candidate regions. 
     It is noted that the second exemplary embodiment exemplifies a case of correcting the parameter P 2  indicating the inter-distance between differentiation reference points. However, deformation correction may be similarly executed for the parameter P 3  and the parameter P 4 . 
     (Third Exemplary Embodiment) 
     Next, the image processing system  1  of a third exemplary embodiment will be explained. The hardware configuration of the image processing system  1  of the third exemplary embodiment is the same as that of the image processing system  1  of the first exemplary embodiment illustrated in  FIG. 1 . Therefore, explanation thereof will be hereinafter omitted, and a given component shared between the first and third exemplary embodiments will be explained while being given the same reference numeral. 
     In the third exemplary embodiment, a predetermined processing is executed in consideration of deformation of an panoramic image in setting parameters, similarly to the second exemplary embodiment. In the second exemplary embodiment, deformation of an image due to curve is regulated. By contrast, a processing related to deformation of an panoramic image in a more sharply curved region will be explained in the third exemplary embodiment. 
     In generating an panoramic image, a cross-sectional orientation is corrected for a sharply curved hollow organ  8  in order to prevent intersection among hollow cross-sections. 
       FIG. 16  is a diagram explaining cross-sectional correction in a sharply curved region. 
     As illustrated in  FIG. 16 , the following technique is used for a sharply curved region of the hollow organ  8 . Simply put, a given cross-sectional concentration point O′ is set in a position that exists outsides a hollow region  81  and simultaneously inwards of the curve of a pathway line  82 , and such an intended hollow plane S n  is selected that passes through a line segment O′Q n  connecting the cross-sectional concentration point O′ and the intended pathway point Q n . 
     When the above cross-sectional correction is executed, the intended hollow plane S n  may be greatly inclined without intersecting at right angle with the tangent line of the pathway line on the intended pathway point Q n . Therefore, it may be difficult to execute linear approximation for the distance between projection coordinates of the intended hollow plane S n , and the adjacent hollow plane S n+1  at corresponding angles unlike the technique of the second exemplary embodiment. 
     In view of the above, according to the present third exemplary embodiment, the longitudinal pixel size dx in the intended pixel p is obtained as the distance between two points in the three dimensional real space that respectively correspond to the intended pixel p and a pixel longitudinally adjacent to the intended pixel p on an panoramic image (hereinafter referred to as “adjacent pixel p next ”). 
       FIG. 17  is a diagram explaining the positional relation among points in the hollow organ  8  that is the source of an panoramic image as a lesion candidate object in the third exemplary embodiment. 
     In a sharply curved region of the hollow organ  8  as illustrated in  FIG. 17 , points in the three dimensional real space, which respectively correspond to the intended pixel p on the hollow plane S n  and the pixel p next  (corresponding to the intended pixel p) on the adjacent hollow plane S n+1 , are respectively referred to as intended pixel corresponding points p′ and p next ′. 
     Then, the length of a circular arc connecting the two intended pixel corresponding points p′ and p next ′ is calculated and the calculated circular arc length is set as a distance dx for approximating the distance dx between the intended pixel corresponding points p′ and p next ′ in the three dimensional real space to be the length of a curve arranged along the pathway line  82 . 
     In response to this, according to the third exemplary embodiment, the pixel deformation (dx/dy) is calculated for each pixel using the pixel deformation calculation processing represented in  FIG. 18  and the deformation adjusted parameters (P 2 _x, P 2 _y) obtained by correcting the parameter P 2  are calculated based on the calculated pixel deformation (dx/dy) when the parameter setting processing of Step S 201  is executed in the lesion candidate detection processing of the first exemplary embodiment (see  FIG. 3 ). 
       FIG. 18  is a flowchart explaining the flow of the pixel deformation calculation processing in the third exemplary embodiment. 
       FIG. 19  is a diagram representing the data to be kept in RAM of the main memory  102  in executing the pixel deformation calculation processing. 
     CPU  101  of the medical image processing device  100  of the third exemplary embodiment reads out the programs and data related to the pixel deformation calculation processing represented in  FIG. 18  from the main memory  102 , and executes the pixel deformation calculation processing based on the read-out programs and data. 
     It is herein assumed that the image data is downloaded from the image database  111  or the like through the network  110  and is stored in the storage device  103  of the medical image processing device  100  on the onset of executing the following processing. 
     In the pixel deformation calculation processing represented in  FIG. 18 , CPU  101  of the medical image processing device  100  firstly loads the panoramic image data, the three dimensional real space coordinate data containing the third dimensional real space coordinates of the corresponding points on the panoramic image, and the coordinate data of the pathway points from the storage device  103  and keeps the loaded data in the main memory  102 , similarly to Step S 301  of the pixel deformation calculation processing represented in  FIG. 10  (Step S 401  and  102   l ,  102   m  and  102   n  in  FIG. 19 ). 
     Next, CPU  101  sequentially scans the respective pixels on the panoramic image  71  and calculates the pixel deformation (dx/dy) of each point (pixel). The flowchart of  FIG. 18  exemplifies a case that the panoramic image is firstly scanned along the lateral direction and then scanned along the longitudinal direction. 
     Similarly to the second exemplary embodiment, CPU  101  firstly determines magnitude of the curve because pixel deformation occurred in the panoramic image  71  depends on how the pathway line  82  is curved, i.e., magnitude of the curve of the hollow organ  8 . 
     CPU  101  firstly calculates the distance between the cross-sectional concentration point O′ and the intended pathway point Q n ′ as the pathway diameter R and keeps the calculated distance in the main memory  102  ( 102   o ′ in  FIG. 19 ). Next, CPU  101  determines magnitude of the curve based on the magnitude of the pathway diameter R (Step S 402 ). 
     When the pathway diameter R is large, this indicates a gentle curved region. When the pathway diameter R is small, this indicates a sharply curved region. 
     Similarly to Step S 303  in  FIG. 10 , CPU  101  determines how the pathway line is curved based on the calculated pathway diameter R (Step S 403 ). For example, when the magnitude of the pathway diameter R is greater than or equal to a predetermined threshold Rt, it is determined that the intended pathway point Q n  is disposed on a gentle curve. When the magnitude of the pathway diameter R is less than the predetermined threshold Rt, it is determined that the intended pathway point Q n  is disposed on a sharp curve. 
     When it is determined that the intended pathway point Q n  is disposed on a gentle curve (Step S 403 ; gentle curve), CPU  101  calculates the x-directional pixel size dx in the intended pixel p and the y-directional pixel size dy in the intended pixel p using the aforementioned equations (8) and (9) and keeps the calculated sizes in the main memory  102 , similarly to Steps S 304 , S 305  and S 306  in  FIG. 9  (Steps S 404  and S 405 ,  102   p  and  102   q  in  FIG. 19 ). Then, CPU  101  calculates the pixel deformation dx/dy and stores the calculated pixel deformation in the array (Step S 406  to Step S 410 ;  102   r  in  FIG. 19 ). 
     On the other hand, the processing proceeds to Step S 407  when it is determined that the intended pathway point Q n  is disposed on a sharp curve (Step S 403 ; sharp curve). In Step S 407 , CPU  101  calculates the length (pixel size dx) of a circular arc between two intended pixel corresponding points p′ and p next ′ illustrated in  FIG. 17 . 
     In other words, CPU  101  firstly calculates distance p′ O′ based on the three dimensional real space coordinate data of the intended pixel corresponding point p′ and the cross-sectional concentration point O′, and sets the calculated distance as radius R′. CPU  101  keeps the calculated radius R′ in the main memory  102  ( 102   x  in  FIG. 19 ). 
     Next, CPU  101  calculates an angle  8  formed by three points, i.e., the intended pixel corresponding point p′, the cross-sectional concentration point O′ and the adjacent pixel corresponding point p next ′ and keeps the calculated angle in the main memory  102  ( 102   y  in  FIG. 19 ). 
     The angle δ is calculated by the following equation (16) where a vector directed from the point O′ to the point P′ is set as a vector O′ p′ and a vector directed from the point O′ to the point p next ′ is set as a vector O′ p next ′. 
     
       
         
           
             
               
                 
                   δ 
                   = 
                   
                     arccos 
                     ⁡ 
                     
                       ( 
                       
                         
                           
                             O 
                             ′ 
                           
                           ⁢ 
                           
                             
                               p 
                               ′ 
                             
                             · 
                             
                               O 
                               ′ 
                             
                           
                           ⁢ 
                           
                             p 
                             next 
                             ′ 
                           
                         
                         
                           
                              
                             
                               
                                 O 
                                 ′ 
                               
                               ⁢ 
                               
                                 p 
                                 ′ 
                               
                             
                              
                           
                           · 
                           
                              
                             
                               
                                 O 
                                 ′ 
                               
                               ⁢ 
                               
                                 p 
                                 next 
                                 ′ 
                               
                             
                              
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   16 
                   ) 
                 
               
             
           
         
       
     
     Subsequently, CPU  101  calculates the length of the circular arc using the following equation (17) and keeps the calculated length in the main memory  102  (Step S 407 ;  102   p  in  FIG. 19 ).
 
dx=R′δ  (17)
 
     Next, CPU  101  calculates the lateral pixel size dy in the intended pixel p using the following equation (18) and keeps the calculated lateral pixel size dy in the main memory  102  (Step S 408 ;  102   q  in  FIG. 19 ).
 
 dy =circumference of intended hollow plane  S   n /matrix size in  y -direction of panoramic image  (18)
 
     CPU  101  calculates the pixel deformation dx/dy based on the pixel sizes dx and dy calculated in Steps S 407  and S 408  (Step S 409 ) and stores the calculated pixel deformation in the array (Step S 410 ;  102   r  in  FIG. 19 ). 
     CPU  101  completes the pixel deformation calculation processing when the pixel deformation dx/dy is calculated for all the pixels by repeating the processing of Steps S 402  to S 410  for each pixel on the panoramic image as described above. 
     The pixel deformation dx/dy for each pixel, calculated in the pixel deformation calculation processing, is referred in executing curvature calculation of Step S 202  in the lesion candidate region detection processing in  FIG. 3 . Calculation of the deformation adjusted parameters (P 2 _x, P 2 _y) is similar to that in the second exemplary embodiment. Therefore, explanation thereof will be hereinafter omitted. 
     Similarly to the display example illustrated in  FIG. 15 , lesion candidate regions can be properly and distinguishably displayed regardless of deformation of an panoramic image (see the lesion candidate regions  713   a ,  713   b  and  713   c  in  FIG. 15 ) by executing the lesion candidate detection processing using the deformation adjusted parameters P 2 _x and P 2 _y in an panoramic image (an panoramic image processed with cross-sectional correction) to be generated by setting the hollow plane S n  about the cross-sectional concentration point O′ in a sharply curved region of a hollow organ as described above. 
     As described above, according to the image processing system  1  of the third exemplary embodiment, the longitudinal pixel size dx of a hollow organ is calculated as the length of a circular arc between adjacent pixel corresponding points in an panoramic image that cross-sectional correction is executed for a curved region. Therefore, it is possible to properly assess the shape of an panoramic image in the real space even if cross-sectional correction is executed for the panoramic image because of a sharp curve included in the curved region. Detection accuracy will be herein enhanced for lesion candidate regions. 
     It is noted that the technique in the third exemplary embodiment for calculating the pixel size dx using a circular arc may be applied to an panoramic image of a relatively gently curved region that has not been processed with cross-sectional correction, such as the panoramic image of the second exemplary embodiment. 
     Further, the third exemplary embodiment also exemplifies a case that correction is executed for the parameter P 2  indicating the inter-distance between differentiation reference points. However, deformation correction may be similarly executed for the parameter P 3  and the parameter P 4 . 
     (Fourth Exemplary Embodiment) 
     The first to third exemplary embodiments exemplify the cases of lesion candidate detection regarding an panoramic image of a hollow organ. However, the image processing device of the invention may be applied for executing other image display methods. In a fourth exemplary embodiment, a case is explained that the invention is applied to a virtual endoscope image. 
       FIG. 20  is a diagram explaining a virtual endoscope image.  FIG. 20  ( a ) illustrates a hollow organ under the condition that the longitudinal direction thereof is illustrated along the vertical direction.  FIG. 20  ( b ) is an example of the virtual endoscope image of the hollow organ in  FIG. 20  ( a ). 
     The virtual endoscope image is an image  75  ( FIG. 20  ( b )) obtained by projecting a view with a predetermined directional range (θ view ) as an angle-of-sight from a given point-of-sight p 0  set in the inside of a hollow region v illustrated in FIG.  20  ( a ) on a planar projection plane s 0 . 
     A pixel value of each point (hereinafter referred to as “intended pixel p”) of the virtual endoscope image  75  is a shadow value given based on the distance between the point-of-sight p 0  and an intended pixel corresponding point p′. For example, the intended pixel corresponding point p′ is a voxel that a virtual light beam called “ray” reaches when the ray is irradiated from the point-of-sight p 0  to the intended pixel p in the three dimensional real space coordinate. The voxel, which is the intended pixel corresponding point p′, has a pixel value within a predetermined threshold range. 
     A method of calculating curvature based on concentration gradient in each point of the virtual endoscope image  75  can be suggested as an example of the methods of calculating curvature with respect to the virtual endoscope image  75 . 
     However, the following two kinds of deformation are produced in the virtual endoscope image  75  to be generated by a generally used perspective projection. 
     The two kinds of deformation are: deformation in accordance with the distance from the point-of-sight p 0  of a projection object; and deformation in accordance with the angle of a direction from the point-of-sight p 0  to the projection object with respect to the projection surface. 
     Therefore, it is necessary to correct the inter-distance between differentiation reference points (parameter P 2 ) to be used for curvature calculation based on the pixel value calculated with the aforementioned method in executing the lesion candidate region detection processing with respect to the virtual endoscope image  75 . 
       FIG. 21  is a diagram explaining deformation due to the distance from the point-of-sight to the projection object.  FIG. 22  is a diagram explaining deformation occurred in the edges of the virtual endoscope image. 
     As illustrated in  FIG. 21 , when the projection object is projected on a projection plane s 0 , the size of the projected image varies in accordance with the distance from the point-of-sight p 0 . Two objects T 1  and T 2 , having the same size, are disposed in the same direction but at different distances from the point-of-sight p 0  towards the projection plane s 0 . In this case, the distance from the point-of-sight p 0  to the object T 1  is set as distance L 1 , whereas the distance from the point-of-sight p 0  to the object T 2  is set as distance L 2 . Further, the sizes of the images to be projected on the projection plane s 0  are respectively set as sizes Δ i  and Δ 2 . When the sizes Δ 1  and Δ 2  are compared, the object T 1  positioned closer to the point-of-sight p 0  is projected on the projection plane s 0  as a larger image (Δ 1 &gt;Δ 2 ). This results in image deformation. Therefore, it is necessary to set the inter-distance between differentiation reference points (parameter P 2 ) in a given intended pixel p so that the same inter-distance between differentiation reference points can be obtained in a given intended pixel corresponding point p′ when curvature calculation is executed. 
     Further, as illustrated in  FIG. 22 , when normal lines are extended towards the objects T 3  and T 4  as the projection targets from the point-of-sight p 0  to the projection plane s 0 , an angle formed by each normal line is set as Θ. Further, the sizes of images to be projected on the projection plane s 0  are respectively set as Δ 3  and Δ 4 . When the sizes Δ 3  and Δ 4  are compared, an image projected on the projection plane s 0  gets larger in proportion to increase in the angle Θ (Δ 4 &gt;Δ 3 ). This results in image deformation. Therefore, it is necessary to correct the value of the inter-distance between differentiation reference points (parameter P 2 ) in the edges of the virtual endoscope image  75  to be greater than that in the center part of the image. 
     Correction of the parameter  2  (inter-distance between differentiation reference points) in the virtual endoscope image  75  generated with perspective projection will be hereinafter explained with reference to  FIGS. 23 ,  24  and  25 . 
       FIG. 23  is a diagram explaining perspective projection. 
       FIG. 24  is a flowchart representing the flow of the processing of calculating the inter-distance between differentiation reference points with respect to the virtual endoscope image  75 . 
       FIG. 25  is a diagram representing the data to be kept in RAM of the main memory  102  in executing the processing of calculating the inter-distance between differentiation reference points. 
     In  FIG. 23 , the following settings are established: p 0  is a point-of-sight; s 0  is a projection plane; Δ is the length of an edge (pixel size) of a pixel positioned in the center of the projection plane s 0  (hereinafter referred to as “center pixel”); L 0  is the distance between the point-of-sight p 0  and the center pixel; and θ 0  is the angle about the point-of-sight p 0  that is formed by the both ends of the center pixel and the point-of-sight p 0 . 
     Further, the following settings are established; p is an intended pixel; Δ′ is the length of the projection object T 1  in an intended pixel corresponding point p′; L′ is the distance between the point-of-sight P 0  and the intended pixel corresponding point p′; and θ is the angle about the point-of-sight p 0  that is formed by the both ends of the intended pixel p and the point-of-sight p 0 . 
     In the processing of calculating the inter-distance between differentiation reference points represented in  FIG. 24 , CPU  101  sets the coordinate of the point-of-sight p 0  and the position and direction of the projection plane s 0  and keeps the set coordinate, position and direction in the main memory  102  (Step S 501 ;  102 A and  102 B in  FIG. 25 ). The projection plane s 0  can be set based on the distance L 0  from the point-of-sight p 0  and a vector connecting the point-of-sight p 0  and the center pixel. 
     Next, CPU  101  calculates the length of an edge of the center pixel on the projection plane s 0  (pixel size Δ) and keeps the calculated length in the main memory  102  (Step S 502 ;  102 C in  FIG. 25 ). The pixel size Δ can be obtained by the following equation (19).
 
Δ=L 0 θ 0   (19)
 
     CPU  101  repeats the processing of the following Steps S 503  to S 506  for each point (intended pixel p) on the projection plane s 0 . 
     First, CPU  101  obtains the coordinate of the intended pixel corresponding point p′ to be projected on the intended pixel p (Step S 503 ). In other words, CPU  101  irradiates the intended pixel p with the ray from the point-of-sight p 0  and obtains the coordinate of an irradiated voxel having a brightness value within a threshold range as the coordinate of the intended pixel corresponding point p′. 
     Next, CPU  101  calculates the length A′ for the intended pixel p at the position of the intended pixel corresponding point p′ and keeps the calculated length in the main memory  102 E (Step S 504 ;  102 E in  FIG. 25 ). 
     As illustrated in  FIG. 23 , the length Δ′ can be calculated based on the distance L′ between the intended pixel corresponding point p′ and the point-of-sight P 0  and the angle θ formed by the both end points of the pixel p′ and the point-of-sight p 0  when the intended pixel p is viewed from the point-of-sight p 0 . Simply put, the length Δ′ is expressed by the following equation (20).
 
Δ′=L′θ  (20)
 
     The angle θ can be herein calculated based on the coordinate of the intended pixel p, the distance L between the intended pixel p and the point-of-sight p 0 , the distance L 0  between the center pixel and the point-of-sight p 0 , and the length Δ of an edge of the center pixel. 
     CPU  101  calculates the inter-distance P 2  between differentiation reference points in the intended pixel p (Step S 505 ). When the inter-distance P 2  between differentiation reference points in the center pixel follows the relation of “P 2 =A×P 1 ” (the aforementioned equation (1)), the inter-distance P 2  between differentiation reference points in the intended pixel p can be expressed by the following equation (21). 
     
       
         
           
             
               
                 
                   
                     P 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   = 
                   
                     
                       A 
                       × 
                       P 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       1 
                       × 
                       
                         Δ 
                         
                           Δ 
                           ′ 
                         
                       
                     
                     = 
                     
                       A 
                       × 
                       P 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       1 
                       × 
                       
                         
                           
                             L 
                             0 
                           
                           ⁢ 
                           
                             θ 
                             0 
                           
                         
                         
                           
                             L 
                             ′ 
                           
                           ⁢ 
                           θ 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   21 
                   ) 
                 
               
             
           
         
       
     
     CPU  101  stores the inter-distance P 2  between differentiation reference points for the intended pixel p calculated in Step S 505  in the array (Step S 506 ;  102 F in  FIG. 25 ). 
     CPU  101  completes the processing of calculating the inter-distance between differentiation reference points represented in  FIG. 24  when the inter-distance P 2  between differentiation reference points are calculated for all the pixels p by repeating the processing of Steps S 503  to S 506  for each pixel in the virtual endoscope image as described above. 
     Subsequently, CPU  101  calculates a curvature value (shape index) using the inter-distance P 2  between differentiation reference points of each pixel calculated with the aforementioned processing steps in the lesion candidate region detection processing represented in  FIG. 3  and detects lesion candidates. 
     As described above, according to the image processing system  1  of the fourth exemplary embodiment, the inter-distance P 2  between differentiation reference points is corrected in the virtual endoscope image to be generated with perspective projection in consideration of image deformation. Then, legion candidates are detected using the corrected inter-distance P 2  between differentiation reference points. 
     Consequently, the shape of the organ surface can be properly assessed even when the lesion candidate detection processing is executed for a virtual endoscope image, and detection accuracy will be thereby enhanced for lesion candidate regions. 
     It is noted that correction of the inter-distance between differentiation reference points in a virtual endoscope image with perspective projection has been described in the fourth exemplary embodiment. However, some of the virtual endoscope images are processed with correction of deformation occurred in the edges of the images (i.e., deformation in accordance with the angle of a direction from the point-of-sight to the projection object with respect to the projection surface) (e.g., JP-A-7-296184). In this case, image deformation depends only on the distance from a point-of-sight. Therefore, it is required to execute correction that depends only on the distance from the point-of-sight for the inter-distance between differentiation reference points (parameter P 2 ). The inter-distance between differentiation reference points is expressed by the following equation (22). 
     
       
         
           
             
               
                 
                   
                     P 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   = 
                   
                     A 
                     × 
                     P 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                     × 
                     
                       
                         L 
                         0 
                       
                       
                         L 
                         ′ 
                       
                     
                   
                 
               
               
                 
                   ( 
                   22 
                   ) 
                 
               
             
           
         
       
     
     The preferred exemplary embodiments of the image processing device according to the invention have been explained above. However, the invention is not limited to the aforementioned exemplary embodiments. For example, the techniques explained in the first to fourth exemplary embodiments may be arbitrarily combined. Further, it is apparent for those skilled in the art that a variety of changes and modifications can be made for the invention without departing from the technical scope disclosed in the present application. It should be understood that those changes and modifications are also incorporated in the technical scope of the invention. 
     DESCRIPTION OF REFERENCE NUMERALS AND SIGNS 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                 1 
                 Image processing system 
               
               
                 100 
                 Medical image processing device 
               
               
                 101 
                 CPU 
               
               
                 102 
                 Main memory 
               
               
                 103 
                 Storage device 
               
               
                 104 
                 Communication I/F 
               
               
                 105 
                 Display memory 
               
               
                 106 
                 I/F 
               
               
                 107 
                 Display device 
               
               
                 108 
                 Mouse (external device) 
               
               
                 109 
                 Input device 
               
               
                 110 
                 Network 
               
               
                 111 
                 Image database 
               
               
                 71 
                 Panoramic image 
               
               
                 713 
                 Lesion candidate region 
               
               
                 72 
                 Parameter setting window 
               
               
                 721 
                 Mode list 
               
               
                 722 
                 Numerical value input box 
               
               
                 8 
                 Hollow organ 
               
               
                 81 
                 Hollow region 
               
               
                 82 
                 Pathway line 
               
               
                 83 
                 Hollow surface 
               
               
                 Q n   
                 Pathway point 
               
               
                 S n   
                 Hollow plane 
               
               
                 P 
                 Intended pixel 
               
               
                 dx 
                 Longitudinal pixel size 
               
               
                 dy 
                 Lateral pixel size 
               
               
                 O 
                 Origin of circle fitted to pathway point 
               
               
                 O′ 
                 Cross-sectional concentration point