Patent Publication Number: US-8542896-B2

Title: Medical image processing device and medical image processing method

Description:
TECHNICAL FIELD 
     The present invention relates to a medical image processing device for extracting and displaying lesion candidates on the basis of a medical image. 
     BACKGROUND ART 
     Tomographic images, etc. of an examinee which are scanned by an X-ray CT (Computed Tomography) apparatus, an MRI (Magnetic Resonance Imaging) apparatus, an ultrasonic apparatus, etc. have been hitherto known as images used for medical diagnosis. There has been developed a computer-aided detection apparatus (Computer-Aided Detection; hereinafter referred to as CAD) in which a medical image as described above is analyzed by using a computer to detect lesion candidates from shade and shadow of the medical image and present the lesion candidates to a medical doctor. CAD automatically detects an image region estimated as a lesion site (hereinafter referred to as lesion candidate region) on the basis of a form characteristic or a density characteristic of the lesion site, and it reduces a labor imposed on the medical doctor. 
     Furthermore, when a large number of cases are required to be read like health check or the like, there is an operator&#39;s requirement of extracting and displaying lesion candidates of plural desired sizes at a time through a series of processing to efficiently perform diagnosis. For example, polyps in a colon region have a characteristic feature, but have various sizes. In general, lesion candidates as medical treatment targets are equal to 5 mm or more in size, and lesion candidates of 10 mm or more have a high risk that they become colon cancers. For example, Patent Document 1 discloses a method of extracting lesion candidates by making an evaluation using a feature amount representing the form of a curved surface (shape index) for a medical image. 
     PRIOR ART DOCUMENT 
     Patent Document 
     
         
         Patent Document 1: JP-A-2006-230910 
       
    
     SUMMARY OF THE INVENTION 
     Problem to be Solved by the Invention 
     However, when lesion candidates as extraction targets are different from one another in size, the optimum value of a parameter for calculating the feature amount representing the form (form exponent; for example, shape index) is different among them. Therefore, the conventional method has a disadvantage that even lesion candidates representing the same form cannot be extracted and displayed at a time through a series of processing when they are different from one another in size. 
     The present invention has been implemented in view of the foregoing problem, and has an object to provide a medical image processing device and a medical image processing method that can extract and display lesion candidates having similarity forms and different sizes at a time through a series of processing. 
     Means of Solving the Problem 
     In order to attain the above object, according to a first invention, a medical image processing device for extracting and displaying lesion candidate regions from a medical image is characterized by comprising: a first extracting unit that makes a first evaluation of a curved surface form for a first medical image to extract a first lesion candidate region; a second extracting unit that makes a second evaluation of a curved surface form for each first lesion candidate region extracted by the first extracting unit to extract a second lesion candidate region; and a display unit that displays the second lesion candidate region extracted by the second extracting unit while the second lesion candidate region is superimposed on a second medical image. 
     According to a second invention, a medical image processing method for extracting and displaying lesion candidate regions from a medical image is characterized by comprising: a first extracting step that makes a first evaluation of a curved surface form for a first medical image to extract a first lesion candidate region; a second extracting unit that makes a second evaluation of a curved surface form for each first lesion candidate region extracted by a first extracting unit to extract a second lesion candidate region; and a display unit that displays the second lesion candidate region extracted by a second extracting unit while the second lesion candidate region is superimposed on a second medical image. 
     Effect of the Invention 
     According to this invention, there can be provided the medical image processing method and the medical image processing device that can extract and display lesion candidates having similarity forms and different sizes at a time through a series of processing. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a hardware construction diagram showing the overall construction of an image processing system  1 . 
         FIG. 2  shows an example of GUI  2  used when lesion candidate extraction processing is executed. 
         FIG. 3  is a flowchart showing the flow of lesion candidate extraction processing in a first embodiment. 
         FIG. 4  is a diagram showing a form exponent (Shape Index). 
         FIG. 5  is a diagram showing a differential distance. 
         FIG. 6  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 107  of  FIG. 3 . 
         FIG. 7  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 108  of  FIG. 3 . 
         FIG. 8  is a diagram showing calculation of a region size. 
         FIG. 9  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 113  of  FIG. 3 . 
         FIG. 10  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 114  of  FIG. 3 . 
         FIG. 11  shows an example of a superimposed image obtained by superimposing a lesion candidate region on a panoramic image. 
         FIG. 12  is a diagram showing a slide display in a hollow organ core line direction. 
         FIG. 13  is a flowchart showing the flow of lesion candidate extraction processing in a second embodiment. 
         FIG. 14  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 207  of  FIG. 13 . 
         FIG. 15  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 208  of  FIG. 13 . 
         FIG. 16  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 213  of  FIG. 13 . 
         FIG. 17  is a diagram showing an example of a lesion candidate region extracted at the stage of step S 214  of  FIG. 13 . 
         FIG. 18  shows an example of a superimposed image obtained by superimposing a lesion candidate region on a virtual endoscopic image. 
         FIG. 19  is a diagram showing a slide display in a hollow organ core line direction. 
         FIG. 20  is a flowchart showing the flow of display processing according to a third embodiment. 
         FIG. 21  shows a display example according to the third embodiment. 
         FIG. 22  is a flowchart showing the flow of display processing according to a fourth embodiment. 
         FIG. 23  shows an example of a display format of a lesion candidate region. 
         FIG. 24  shows an example of the display format of the lesion candidate region. 
         FIG. 25  shows an example of the display format of the lesion candidate region. 
     
    
    
     BEST MODES FOR CARRYING OUT THE INVENTION 
     Preferable embodiments according to the present invention will be described in detail with reference to the accompanying drawings. 
     First Embodiment 
     First, the construction of an image processing system  1  to which a medical image processing device according to the present invention is applied will be described. 
     As shown in  FIG. 1 , the image processing system  1  includes a medical image processing device  100  having a display device  107  and an input device  109 , and an image data base  111  and a medical image scanning device  112  which are connected to the medical image processing device  100  through a network  110 . 
     The medical image processing device  100  is a image diagnosing computer installed in a hospital or the like, and it functions as a computer-aided detection device (CAD) for analyzing a medical image, detecting a lesion candidate (s) from shade and shadow of the medical image and presenting the lesion candidate (s) to a medical doctor. The medical image processing device  100  has CPU  101  (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 equipment such as a mouse  108  or the like, and the respective parts are connected to one another through a bus  113 . 
     CPU  101  calls up a program stored in the main memory  102 , the storage device  103  or the like into a work memory area on RAM of the main memory  102  to execute the program, and controls the operation of the respective parts connected through the bus  113  to implement various kinds of processing executed by the medical image processing device  100 . 
     Furthermore, CPU  101  executes processing described later concerning extraction of a lesion candidate region in the first embodiment (see  FIG. 3 ). 
     The main memory  102  comprises ROM (Read Only Memory), RAM (Random Access Memory), etc. ROM permanently holds programs such as a boot program of the computer, programs such as BIOS, etc., data, etc. RAM temporarily holds programs loaded from ROM, the storage device  103 , etc., data, etc. and has a work area which is used to perform various kinds of processing by CPU  101 . 
     The storage device  103  is a storage device for reading/writing data from/into HDD (hard disk drive) or another storage medium, and programs to be executed by CPU  101 , data required to execute programs, OS (operating system), etc. are stored in the storage device  103 . With respect to the programs, a control program corresponding to OS and application programs are stored. Program codes of these programs are read out by CPU  101  as occasion demands, shifted to RAM of the main memory  102  and executed as various kinds of means. 
     The communication I/F  104  has a communication control device, a communication port, etc., and mediates communications with the medical image processing device  100  and the network  110 . The communication I/F  104  controls communication with the image data base  111 , another computer or the medical image scanning device  112  through the network  110 . I/F  106  is a port for connection to peripheral equipment, and transmits/receives data to/from the peripheral equipment. For example, input devices such as the mouse  108 , etc. may be connected through I/F  106 . 
     The mouse  108  indicates any position on a display screen by moving operation or operation of a button, a wheel or the like, and pushes a software switch, etc., and outputs the operation signal corresponding to the operation through I/F  106  to CPU  101 . The display memory  105  is a buffer for temporarily accumulating display data input from CPU  101 . The accumulated display data are output to the display device  107  at a predetermined timing. 
     The display device  107  comprises a display device such as a liquid crystal panel, a CRT monitor or the like, and a logic circuit for executing display processing in cooperation with the display device, and it is connected to CPU  101  through the display memory  105 . Under the control of CPU  101 , the display device  107  displays the display data accumulated in the display memory  105  on the display device. 
     The input device  109  is an input device such as a keyboard or the like and outputs to CPU  101  various kinds of instructions and information input by an operator such as ID information for specifying medical images, diagnosis reports of medical images displayed on the display device  107 , etc., for example. The operator dialogically operates the medical image processing device  100  by using the external equipment such as the display device  107 , the input device  109 , the mouse  108 , etc. 
     The network  110  contains various kinds of communication networks such as LAN (Local Area Network), WAN (Wide Area Network), Intranet, Internet, etc., and mediates communication connection between the image data base  111 , a server, another information equipment or the like and the medical image processing device  100 . 
     The image data base  111  accumulates and stores medical images scanned by the medical image scanning device  112 , and it is provided to a server or the like in a hospital, a medical center or the like. In the image processing system  1  shown in  FIG. 1 , the image data base  111  is connected to the medical image processing device  100  through the network  110 , however, the image data base  111  may be provided to the storage device  103  in the medical image processing device  100 , for example. 
     The medical image scanning device  112  is an apparatus for picking up tomographic images of an examinee such as an X-ray CT apparatus, an MRI apparatus, an ultrasonic apparatus, a scintillation camera device, PET (Positron Emission Tomography) apparatus, SPECT (Single Photon Emission Computed Tomography) apparatus or the like, and it is connected to the image data base  111  or the medical image processing device  100  through the network  110 . 
     Medical images handled by the image processing system  1  of this invention contain tomographic images, panoramic images of hollow organs and virtual endoscopic images of examinees. The panoramic image is obtained by displaying the inside of an internal organ so that the hollow organ is developed around the core line of the hollow organ (see  FIG. 11 ), and the virtual endoscopic image is obtained by displaying the inside of the hollow organ according to a display method based on a central projection method from a virtual viewing point provided to the inside of the hollow organ (see  FIG. 18 ). 
     Next, the operation of the image processing system  1  will be described with reference to  FIGS. 2 to 12 . 
     CPU  101  of the medical image processing device  100  reads out a program concerning lesion candidate extraction processing and data from the main memory  102 , and executes the lesion candidate extraction processing on the basis of this program and the data. 
     When execution of the following lesion candidate extraction processing is started, it is assumed that image data are taken from the image data base  111  or the like through the network  110  and the communication I/F  104  and stored into the storage device  103  of the medical image processing device  100 . Furthermore, when an execution start instruction of the lesion candidate extraction processing is input from the input device  109  or the like, for example, GUI  2  shown in  FIG. 2  is read out from the storage device  106  and displayed on the display device  107 . 
     GUI  2  shown in  FIG. 2  has various kinds of input frames for inputting various conditions, set values or instruction required when a lesion candidate region is extracted, and an image display region  7  for displaying an extraction result, etc. An operator can dialogically input various conditions, etc. by operating the input device  109 , the mouse  108  or the like while referring to a content displayed on GUI  2 . 
     On GUI  2  are displayed a data read-in button  3 , an input frame  4  for inputting an initial differential distance, an input frame  5  for inputting an initial form exponent threshold value, an input frame  6  for inputting a form exponent threshold value, an image display region  7  for displaying various kinds of images such as a medical image as a target, an extraction result of the lesion candidate extraction region, etc., an input frame  8  for instructing and inputting the size of the lesion candidate region to be superimposed and displayed, a scroll bar  9  for varying a value to be input to the input frame  8 , etc. 
     In the lesion candidate extraction processing of  FIG. 3 , when the data read-in button  3  of GUI  2  of  FIG. 2  is first clicked, CPU  101  executes the processing of reading image data. CPU  101  displays an image selection window on the display device  107  so that plural selection target images are displayed in a list or thumb nail display style on the image selection window, and accepts selection of an image from the operator. When the operator selects a desired image, CPU  101  reads out selected image data from the storage device  103  and holds the image data into the main memory  102  (step S 101 ). 
     In this embodiment, it is assumed that image data of a hollow organ region such as a colon or the like are selected. Furthermore, the image data read at this stage are assumed as volume image data obtained by stacking plural tomographic images. 
     Subsequently, CPU  101  extracts a core line from the image data read in step S 101  (step S 102 ). As disclosed in JP-A-2006-42969, the extraction of the core line is performed by tracking a start point, a terminal point and passing points indicated in the hollow organ region of the displayed volume image data. 
     Subsequently, CPU  101  creates a display image by using core line information extracted in step S 102 . In this case, it is assumed that a panoramic image  71  is created as a display image (step S 103 ; see  FIG. 11 ). Details of the creation of the panoramic image  71  are disclosed in the Patent Document (U.S. Pat. No. 3,627,066), and the description thereof is omitted. 
     Subsequently, CPU  101  sets a parameter P 1  for calculating a form exponent S for the overall panoramic image  71  created in step S 103  (step S 104 ). Here, the form exponent S is an index for estimating the state of the curved surface of the image, and so-called Shape Index is used as an example. The form exponent S is represented by the following mathematical expression (1). The parameter P 1  is, for example, a differential distance for calculating a differential value at a point of interest, and used when the form exponent S is calculated (see the following mathematical expression (3)). As the parameter P 1 , may be used a value which is empirically determined in advance or any numerical value input to the input frame  4  of GUI  2  of  FIG. 2 . CPU  101  stores the set parameter P 1  into the main memory  102 . 
                   [     Expression   ⁢           ⁢     (   1   )       ]                           S   =       1   2     -       1   π     ⁢     arctan   ⁡     (         λ   max     +     λ   min           λ   max     -     λ   min         )                   (   1   )               
In the mathematical expression (1), λmax, λmin represent the maximum value and minimum value of a main curvature at each point on a curved surface, and they are calculated by the following mathematical expression (2).
 
                   [     Expression   ⁢           ⁢   2     ]                               λ   max     ≡       1   2     ⁡     [       f   xx     +     f   yy     +           (       f   xx     +     f   yy       )     2     -     4   ⁢     (         f   xx     ⁢     f   yy       -       f   xy     ⁢     f   xy         )             ]         ⁢     
     ⁢       λ   min     ≡       1   2     ⁡     [       f   xx     +     f   yy     -           (       f   xx     +     f   yy       )     2     -     4   ⁢     (         f   xx     ⁢     f   yy       -       f   xy     ⁢     f   xy         )             ]                 (   2   )               
In the mathematical expression (2), fxx, fyy, fxy represent secondary partial derivatives of f(x, y) at a pixel-of-interest p, and it is calculated according to the following mathematical expression (3) by using the coordinate (x, y) of the pixel-of-interest p and depth data f(x, y) at the pixel p. The depth data f(x, y) represents the distance on a three-dimensional coordinate from the surface of a hollow organ to the core line thereof at a coordinate (x, y) in a real space of each point (each pixel) of the wall of the hollow organ represented as a panoramic image. The depth data f (x, y) is generated when the panoramic image  71  is created.
 
     
       
         
           
             
               
                 
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     As shown in  FIG. 4 , the Shape Index (form exponent S) has a value which continuously varies from 0 to 1, and different curved surface states correspond to the respective values. That is, a concave hemisphere corresponds to a value “0” of shape Index, and the value of Shape Index represents a concaved semicircular column, a saddle-shaped plane/flat plane, a convex semicircular column and a convex hemisphere in this order as the value of Shape Index increases from “0”. The convex hemisphere corresponds to the value “1” of Shape Index. 
     When the form exponent S of a convex surface  601  shown in  FIG. 5  is determined, the value of the form exponent S is dependent on the differential distance (parameter P 1 ). The form exponent S has the maximum value when the differential distance is equal to the same level as the width of the curved surface (unevenness). When the differential distance is smaller than the width of the unevenness as indicated by an arrow  602  of  FIG. 5 , the form exponent S of a substantially planar surface is determined, and thus the form exponent S has a value in the neighborhood of “0.5”. On the other hand, when the width of the unevenness and the differential distance are equal to the same level as indicated by an arrow  603  of  FIG. 5 , the gradient of the convex surface can be captured when a secondary derivative function is calculated. Therefore, the form exponent S has a value in the neighborhood of “1”, and thus it represents that the form is close to the convex hemisphere. 
     As described above, the parameter P 1  set in step S 104  is used when the main curvature λmax, λmin are calculated, and thus the calculation result varies in accordance with the value of the parameter P 1  even when the form exponent S for the same pixel is calculated. 
     In the lesion candidate extraction processing of the present invention, the form exponent S is repetitively calculated in a series of processing. In the following description, a form exponent S which is first calculated (step S 106 ) is called as an initial form exponent S 0 , and a form exponent which is calculated at a subsequent stage (step S 112 ) is called as S n  (n=1, 2, 3, . . . ). 
     CPU  101  sets a threshold value for the initial form exponent S 0  (hereinafter referred to as initial form exponent threshold value) (step S 105 ). As the initial form exponent threshold value may be used a value which is empirically determined in advance or any numerical value input to the input frame  5  of GUI  2  of  FIG. 2 . CPU  101  stores the set initial form exponent threshold value into the main memory  102 . In this embodiment, since a convex lesion candidate (polyp) is extracted, it is assumed that the initial form exponent threshold value represents the lower limit value. 
     CPU  101  calculates the initial form exponent S 0  for each pixel of the panoramic image  71  created in step S 103  by using the differential distance (parameter P 1 ) set in step S 104  (step S 106 ). 
     CPU  101  executes threshold value processing on the form exponent S 0  calculated in step S 106  by using the initial form exponent threshold value set in step S 105  to extract a region falling into a threshold value range, and stores the region into the main memory  102  (step S 107 ). 
     Specifically, CPU  101  sets, as lesion candidate regions, pixels having form exponents S 0  which are above the set initial form exponent threshold value. Through these stages, in order to roughly extract the lesion candidate regions, it is desired to set the initial form exponent threshold value set in step S 105  to a relatively low value like “0.75”, for example (see  FIG. 2 ). 
     At this stage, some regions  501 ,  502 ,  503 , . . . in the panoramic image are extracted like regions indicated by hatched lines of an image  711  of  FIG. 6 . With respect to a convex surface whose size remarkably exceeds the set parameter P 1  (differential distance), the value of the calculated initial form exponent S 0  is smaller, so that it is set as an out-of-target of lesion candidate and thus it is not extracted. All the hatched regions in  FIG. 6  are regions extracted in the processing of step S 107 , however, reference numerals of some regions in  FIG. 6  are omitted. 
     With respect to each region extracted in step S 107 , CPU  101  calculates various kinds of feature amounts such as the degree of circularity, a major-axis/minor-axis ratio, etc. of the region. With respect to the calculated feature amounts, only regions falling into the preset threshold value range are extracted, and false-positive regions are deleted (step S 108 ). The regions  501 ,  502 ,  503 ,  504 ,  507 ,  508 ,  512 ,  514  remaining at this stage are shown in  FIG. 7 . 
     In the image  712  shown in  FIG. 7 , out of the extracted regions shown in  FIG. 6 , regions which are small in major-axis/minor-axis ratio and have forms relatively close to a circle are extracted. For example, an average value &lt;S 0 &gt; of the initial form exponents S 0  of the respective pixels in the region  501  of  FIG. 7  is assumed to represent “0.75”. 
     Subsequently, CPU  101  calculates the region size for each region extracted in step S 108  (step S 109 ). In the following description, a number i is affixed to an extracted lesion candidate region, the i-th lesion candidate region is referred to as a region i, and the region size of the region i is referred to as Li. The region size Li may be set to the maximum value of the distances among all the pixels belonging to the end (contour) of the region I, for example, as shown in  FIG. 8 . CPU  101  holds the region size Li calculated for each region i into the main memory  102 . 
     Subsequently, CPU  101  resets a parameter di for each lesion candidate region extracted in step S 108  by using the region size Li calculated in step S 109 , and holds the parameter di into the main memory  102  (step S 110 ). The parameter di is a differential distance used to re-calculate the form exponent S n , and it is calculated according to the following mathematical expression (4), for example. α of the mathematical expression (4) represents a coefficient which is empirically determined in advance.
 
[Expression 4]
 
 di=αLi   (4)
 
     Subsequently, CPU  101  resets the threshold value for the re-calculated form exponent S n  (step S 111 ). A value which is empirically determined in advance may be used as the threshold value, and any numerical value input to the input frame  6  of GUI  2  of  FIG. 2  may be used as the threshold value. The threshold value set in step S 111  is referred to as the threshold value of the re-calculated form exponent S n . CPU  101  holds the set threshold value of the re-calculated form exponent S n  into the main memory  102 . In this case, it is assumed that a value of “0.9” is input to the input frame  6  as shown in  FIG. 2 . 
     CPU  101  re-calculates the form exponent S n  for each region extracted in step S 108  by using the parameter di set in step S 110  (step S 112 ). Here, the form exponent S n  is calculated according to the above mathematical expressions (1), (2) and (3). However, the parameter P 1  contained in the mathematical expression (3) is assumed to be replaced by the reset parameter di. 
     Here, CPU  101  may execute expansion processing on each lesion candidate region extracted in step S 108  and then re-calculate the form exponent S n . The expansion processing is the processing of expanding the edge of the region i by the amount corresponding to one to several pixels. The region which has been subjected to the expansion processing is set as a calculation target of the form exponent S n , whereby the form exponent S n  can be re-calculated for even pixels which are excluded for a reason such as nonconformity of the parameter P 1  or the like at the calculation stage (step S 106 ) of the initial form exponent S 0 , thereby enhancing the extraction precision. Not limited to the expansion processing, a region as a calculation target of the form exponent S n  may be arbitrarily expanded. 
     CPU  101  executes threshold value processing on the form exponent S n  calculated in step S 112  by using the threshold value set in step S 111 , and extracts a region falling in the threshold value range (step S 113 ). 
     At this stage, some regions  501 ,  502 ,  503 ,  504 ,  507 ,  508 ,  512  and  515  are extracted in the panoramic image  713  like hatched regions of the image  713  of  FIG. 9 . In the case of the region  501  as an example, the average value &lt;S n &gt; of form exponents S n  of respective pixels in the region  501  is corrected to “0.98” through the processing from step S 110  to step S 113 . The average value &lt;S 0 &gt; of the initial form exponents S 0  of the respective pixels in the corresponding region  501  of  FIG. 7  which are extracted at the stage before the processing from step S 110  to step S 113  is executed is equal to “0.75”. 
     CPU  101  calculates various kinds of feature amounts such as the degree of circularity, a major-axis/minor-axis ratio, etc. of a region for each lesion candidate region extracted in step S 113 . With respect to the calculated feature amounts, only regions falling in the preset threshold value range are extracted, and false-positive regions are deleted (step S 114 ). The lesion candidate regions  501 ,  504 ,  507  remaining at this stage are shown in  FIG. 10 . 
     CPU  101  re-calculates the region size Li for each lesion candidate region i extracted in step S 114 , and holds it into the main memory  102  (step S 115 ). The region size Li is determined as in the case of the step S 109 . 
     The processing from steps S 110  to S 115  may be executed only once or repetitively executed at plural times. When the processing is repeated at plural times, as shown in step S 116 , CPU  101  compares the region size of the lesion candidate region re-extracted in the previous loop with the region size of the lesion candidate region re-extracted in the present loop, and shifts the processing to step S 117  when the difference therebetween is equal to a predetermined value or less. 
     In step S 117 , CPU  101  creates a superimposed image  715  obtained by superimposing each lesion candidate region extracted in step S 114  on the panoramic image  71  created in step S 103 . Each lesion candidate region of the superimposed image  715  is assumed to be supplied with a different color value in accordance with the value of the form exponent S n  re-calculated in step S 112  (step S 117 ). CPU  101  displays the superimposed image  715  created in step S 117  in the image display region  7  within GUI  2  displayed on the display device  107  (step S 108 ). 
     For example, in the superimposed image  715 , the re-extracted lesion candidate regions  501 ,  504 ,  507  are superimposed and displayed on the panoramic image  71  as shown in  FIG. 11 . The lesion candidate regions  501 ,  504  and  507  are different from one another in region size. However, the values of the re-calculated form exponents S n  thereof are equal to the set threshold value or more (for example, “0.9” or more), and thus they have substantially similarity forms. Furthermore, it is assumed that the lesion candidate regions  501 ,  504  and  507  have substantially the same form exponent S n  and thus are displayed with the same color. 
     In step S 117 , the lesion candidate regions in which color values are superimposed may be set to all the lesion candidate regions extracted in step S 114  or to lesion candidate regions whose region sizes are equal to or more than a predetermined region size. The region size of the lesion candidate region to be displayed may be set in accordance with a value which is input to the input frame  8  of GUI  2  of  FIG. 2  by an operator. In this case, CPU  101  refers to the region size Li calculated in step S 115 , supplies color values to the lesion candidate regions whose region sizes Li are equal to or larger than the region size input to the input frame  8 , whereby they are superimposed and displayed. 
     A numeral value corresponding to a moving operation of the scroll bar  9  is input to the input frame  8  shown in GUI  2  of  FIG. 2 . In the example shown in  FIG. 2 , “6” mm is input to the input frame  8 , and thus only the lesion candidate regions having the region sizes Li of 6 mm or more are selected, and superimposed and displayed. 
     The created superimposed image  715  may be displayed so as to be slidable at a predetermined feeding width in the core line direction of the hollow organ. In this case, CPU  101  may control the feeding width so as to reduce the feeding width to the next frame when a displayed frame (a part of the superimposed image) contains a lesion candidate region and increase the feeding width to the next frame when no lesion candidate region is contained. 
     For example,  FIG. 12  is a diagram showing two slide-displayed continuous frames arranged in the vertical direction, wherein (A) shows a portion containing no lesion candidate region and (B) shows a portion containing a lesion candidate region. 
     When the slide-display feeding width at the portion containing no lesion candidate region is represented by A as shown in  FIG. 12(A)  and the slide-display feeding width at the portion containing the lesion candidate region is represented by Δ′ as shown in  FIG. 12(B) , CPU  101  controls the feeding width so that Δ is larger than Δ′ (Δ&gt;Δ′). As described above, the slide-display is performed so that the feeding width at the portion containing the lesion candidate region is reduced, whereby more attention is paid to the portion containing the lesion candidate region. 
     As described above, in the image processing system  1  according to the first embodiment, the medical image processing device  100  executes the processing of extracting a lesion candidate region from a medical image (panoramic image  71 ). In the lesion candidate extraction processing, CPU  101  calculates the form exponent S 0  for each pixel of the overall panoramic image  71  by using an initial differential distance (parameter P 1 ), and subjects the calculated form exponent S 0  to the threshold value processing to extract the lesion candidate region. Furthermore, CPU  101  makes an evaluation of the size of the lesion candidate region and the other feature amounts to thereby delete false-positive regions. Thereafter, CPU  101  calculates the region size Li for each lesion candidate region, and resets the parameter di (differential distance) corresponding to the region size Li. Then, CPU  101  re-calculates the form exponent S n  for each lesion candidate region by using the reset parameter di. Furthermore, CPU  101  executes the threshold value processing on the re-calculated form exponent S n  and makes an evaluation of the size of the lesion candidate region and the other feature amounts, whereby the false-positive regions are deleted and the lesion candidate regions are re-extracted. Thereafter, CPU  101  superimposes and displays the re-extracted lesion candidate region on the panoramic image  71  in a display style (color value or the like) which is different every form exponent S n . 
     Accordingly, the optimum differential distance di corresponding to the region size Li of the lesion candidate region is applied so that the form of each lesion candidate region can be estimated. Therefore, with respect to even lesion candidate regions having the same form and different sizes, the lesion candidate regions concerned can be extracted at a time through a series of processing, and superimposed and displayed on the panoramic image. Furthermore, they are superimposed and displayed in the display style (color value) or the like which is different in accordance with the form, and thus the lesion candidate regions can be displayed in the same display style even when they are different in size from one another, but they have the similarity form, so that the lesion candidates can be easily observed. 
     In the above example, the threshold value used in the threshold processing of the form exponents S 0 , S n  is set as the lower limit value. However, it may be set as the upper limit value or the range in accordance with the form to be extracted. Furthermore, in the false-positive deletion processing of steps S 108  and S 114 , the major-axis/minor-axis ratio and the degree of circularity are estimated as feature amounts, however, the present invention is not limited to them. CT values, etc. of a region of interest may be set as feature amounts, and false-positive regions may be determined on the basis of these feature amounts. 
     Second Embodiment 
     Next, the image processing system  1  according to the second embodiment will be described. In the second embodiment, a method of extracting a lesion candidate region described with reference to the first embodiment is applied to a virtual endoscopic image. Furthermore, the hardware construction of the image processing system  1  according to the second embodiment is the same as the image processing system  1  according to the first embodiment of  FIG. 1 , and the description thereof is omitted. The same parts are represented by the same reference numerals. 
     The lesion candidate extraction processing executed in the medical image processing device  100  according to the second embodiment will be described. 
     CPU  101  of the medical image processing device  100  according to the second embodiment reads out a program and data concerning the lesion candidate extraction processing shown in  FIG. 13  from the main memory  102 , and executes the lesion candidate extraction processing on the basis of the program and the data. 
     In the lesion candidate extraction processing of  FIG. 13 , as in the case of the steps S 101  to S 102  of the lesion candidate extraction processing ( FIG. 3 ) in the first embodiment, when the data read-in button  3  of GUI  2  of  FIG. 2  is clicked, CPU  101  executes the read-in processing of image data. CPU  101  reads out the selected image data from the storage device  103  and holds the image data into the main memory  102  (step S 201 ). Furthermore, CPU  101  extracts a core line from the read-in image data (step S 202 ). 
     Subsequently, CPU  101  creates a display image by using the core line information extracted in step S 202 . In this case, it is assumed that a virtual endoscopic image  72  is created as a display image (step S 203 ; see  FIG. 18 ). The virtual endoscopic image  72  is defined as an image obtained when an aspect which is viewed from any viewing point set in a hollow organ region with some range of direction set as a visual field angle is projected onto a planar projection plane. The detailed creation of the virtual endoscopic image  72  is disclosed in Patent Document (JP-A-7-296184) or the like, and the description thereof is omitted. 
     CPU  101  sets the parameter P 1  for calculating the initial form exponent S 0  for the virtual endoscopic image  72  created in step S 203  (step S 204 ). Here, as in the case of the step S 104  of the first embodiment, the set parameter P 1  is a differential distance for determining a different value at a point of interest, for example. A value which is empirically determined in advance may be used as the parameter P 1 , or any numerical value input to the input frame  4  of GUI  2  of  FIG. 2  may be used as the parameter P 1 . CPU  101  stores the set parameter P 1  into the main memory  102 . 
     Subsequently, as in the case of the step S 105  of the first embodiment, CPU  101  sets the initial form exponent threshold value (step S 205 ). 
     CPU  101  calculates the form exponent S 0  for each pixel of the overall virtual endoscopic image  7  created in step S 203  by using the differential distance (parameter P 1 ) set in step S 204  (step S 206 ). A value represented by the above mathematical expression (1) is used as the initial form exponent S 0  as in the case of the first embodiment. 
     CPU  101  executes the threshold value processing on the form exponent S 0  calculated in step S 206  by using the initial form exponent threshold value set in step S 205 , and extracts regions falling in the threshold value range (step S 207 ). 
     At this stage, some lesion candidate regions  801 ,  802 ,  803 , . . . in the virtual endoscopic image  72  are extracted as shown in the image  721  of  FIG. 14 . CPU  101  calculates the various kinds of feature amounts such as the degree of circularity, the major-axis/minor-axis ratio, etc. of the region for each lesion candidate region extracted in step S 207 . With respect to the feature amounts, only the regions falling in the preset threshold value range are extracted, and false-positive regions are deleted (step S 208 ). Lesion candidate regions  801 ,  802 ,  803 ,  804 ,  806  remaining at this stage are shown in  FIG. 15 . 
     In the example shown in  FIG. 15 , regions  801 ,  802 ,  803 ,  804  and  806  which are small in major-axis/minor-axis ratio and relatively near to a circle in form are extracted out of the regions shown in  FIG. 14 . For example, it is assumed that the average value &lt;S 0 &gt; of the initial form exponents S 0  of the respective pixels in the region  802  of  FIG. 15  represents “0.75”. 
     Subsequently, CPU  101  calculates the size (region size Li) for each region extracted in step S 208  (step S 209 ). The calculation of the region size Li is the same as the first embodiment. CPU  101  holds the region size Li calculated for each region into the main memory  102 . 
     Subsequently, CPU  101  resets the parameter di of each lesion candidate region extracted in step S 208  by using the region size Li calculated in step S 209 , and holds the parameter di into the main memory  102  (step S 210 ). The parameter di is determined by using the above mathematical expression (4) as in the case of the first embodiment, and it is set to the value corresponding to the region size Li of each lesion candidate region i. 
     Subsequently, as in the case of the step S 111  of the first embodiment, CPU  101  resets the threshold value for the re-calculated form exponent S n  (step S 211 ). Furthermore, as in the case of the step S 112  of the first embodiment, CPU  101  re-calculates the form exponent S n  for each region extracted in step S 208  by using the parameter di set in the step S 210  (step S 212 ). 
     Furthermore, as in the case of the step S 113  of the first embodiment, CPU  101  executes the threshold value processing on the form exponent S n  re-calculated in step S 212  by using the threshold value set in step S 211 , and extracts regions falling in the threshold range (step S 213 ). 
     At this stage, some regions  801 ,  802 ,  803 ,  804  and  806  are extracted in the virtual endoscopic image  72  like hatched regions of the image  723  of  FIG. 16 . Taking the region  802  as an example, the average value &lt;S n &gt; of the form exponents S n  of the respective pixels in the region  802  is corrected to “0.98” through the processing from step S 210  to step S 213 . The average value &lt;S 0 &gt; of the initial form exponents S 0  of the respective pixels in the corresponding region  802  of  FIG. 15 , which is extracted at the stage before the processing from step S 210  to step S 213 , is equal to “0.75”. 
     As in the case of the step S 114  of the first embodiment, CPU  101  calculates the various kinds of feature amounts of a region such as the degree of circularity, the major-axis/minor-axis ratio, etc. for each region extracted in step S 213 . With respect to the calculated feature amounts, only regions falling in the preset threshold value range are extracted, and false-positive regions are deleted (step S 214 ). The regions  801 ,  802  and  803  remaining at this stage are shown in  FIG. 17 . 
     CPU  101  re-calculates the region size Li for each lesion candidate region i re-extracted in step S 214 . The region size Li is determined as in the case of the step S 209 . 
     As in the case of the first embodiment, the processing of steps S 210  to S 215  may be executed only once or repeated at plural times. When it is repeated at plural times, as indicated in the step S 216 , the region size of the lesion candidate region re-extracted in the previous loop is compared with the region size of the lesion candidate region re-extracted in the present loop, and when the difference therebetween is equal to a predetermined value or less, the processing shifts to step S 217 . 
     In step S 217 , CPU  101  creates a superimposed image  725  obtained by superimposing each lesion candidate region extracted in step S 214  on the virtual endoscopic image  72  created in step S 203 . It is assumed that a color value which is different in accordance with the value of the form exponent S n  re-calculated in step S 212  is given to each lesion candidate region of the superimposed image  725  (step S 217 ). Then, CPU  101  displays the superimposed image  725  created in step S 217  on the image display region  7  in GUI  2  (step S 218 ). 
     For example, in the superimposed image  725 , the re-extracted lesion candidate regions  801 ,  802  and  803  are displayed on the virtual endoscopic image  72  as shown in FIG.  18 . The lesion candidate regions  801 ,  802  and  803  are different in region size, however, the values of the re-calculated form exponents S n  thereof are equal to a set threshold value or more (for example, “0.9” or more), so that they have substantially similarity forms. Furthermore, the lesion candidate regions  801 ,  802  and  803  have substantially the same form, and thus they are displayed with the same color. 
     As in the case of the first embodiment, in step S 217 , the lesion candidate regions on which the color values are superimposed may be applied to all the lesion candidate regions extracted in step S 214 . However, they may be applied to only regions having a predetermined region size or more out of the above lesion candidate regions. 
     Furthermore, as in the case of the first embodiment, the created superimposed image  725  may be slide-displayed at a predetermined feeding width in the core line direction of the hollow organ. In this case, CPU  101  may control the feeding width so that the feeding width to the next frame is reduced when the displayed frame (a part of the superimposed image) contains a lesion candidate region, and the feeding width to the next frame is increased when the displayed frame contains no lesion candidate region. 
     For example,  FIG. 19  is a diagram showing two slide-displayed continuous frames, wherein (A) shows a portion containing no lesion candidate region, and (B) shows a portion containing a lesion candidate region. 
     As shown in  FIG. 19(A) , the corresponding points of the continuous frames  726  and  727  containing no lesion candidate region are represented by  726   a ,  727   a . Furthermore, as shown in  FIG. 19(B) , the corresponding points of the continuous frames  728 ,  729  containing a lesion candidate region are represented by  728   a ,  729   a . In this case, CPU  101  controls to increase the movement amount of a viewing point more greatly at the portion containing no lesion candidate region of  FIG. 19(A)  as compared with the portion containing a lesion candidate region of  FIG. 19(B) . As described above, the slide-display is executed while the feeding width of the portion containing the lesion candidate region is reduced, whereby more attention is paid to the portion containing the lesion candidate region. 
     As described above, according to the second embodiment, the same processing as the first embodiment (extraction of the lesion candidate region from the panoramic image) is executed on the virtual endoscopic image  72 . 
     Accordingly, with respect to even the virtual endoscopic image, lesion candidate regions which have the same form and different sizes can be extracted at a time through a series of processing, and superimposed and displayed. 
     Third Embodiment 
     Next, the image processing system  1  according to a third embodiment will be described. The hardware construction of the image processing system  1  according to the third embodiment is the same as the image processing system  1  according to the first embodiment of  FIG. 1 , and thus the description thereof is omitted. The same parts are represented by the same reference numerals and described. 
     In the third embodiment, the lesion candidate regions extracted from the panoramic image  71  in the lesion candidate extracting processing (the steps S 101  to S 117  of  FIG. 3 ) of the first embodiment are reflected to the virtual endoscopic image  72 . 
     The image processing system  1  according to the third embodiment will be described hereunder with reference to  FIGS. 20 and 21 . 
     In the display processing of the third embodiment shown in  FIG. 20 , CPU  101  first extracts a lesion candidate region from a panoramic image  71  in the steps S 101  to S 116  of the lesion candidate extraction processing of  FIG. 3 , and also stores a re-calculated form exponent S n  (step S 112  of  FIG. 3 ) into the main memory  102  (step S 301 ). The color value superimposition processing of the step S 117  and the superimposition image display processing of the step S 118  in  FIG. 3  may be omitted. 
     Furthermore, CPU  101  acquires, for example, coordinate information such as a real space coordinate or the like for the lesion candidate region extracted in step S 301  and holds it into the main memory  102  (step S 302 ). 
     Subsequently, CPU  101  creates the virtual endoscopic image  72  according to the processing of the steps S 201  to S 203  of  FIG. 13  (step S 303 ). Then, CPU  101  determines whether the coordinate corresponding to the coordinate information obtained in step S 302  (the real space coordinate of the lesion candidate region extracted from the panoramic image  71 ) is contained in the real space coordinate of the inner wall displayed on the virtual endoscopic image  72  created in step S 303  (step S 304 ). 
     When it is determined in step S 304  that the coordinate corresponding to the coordinate information (lesion candidate region) obtained in step S 302  is contained in the real space coordinate of the inner wall displayed on the virtual endoscopic image  72 , CPU  101  creates a superimposed image  732  superimposed with the color value representing the lesion candidate region (see  FIG. 21 ) at the corresponding coordinate of the inner wall of the virtual endoscopic image  72 . Here, the color value representing the lesion candidate region is set to the color value corresponding to the form exponent S n  of each region stored in the main memory  102  in step S 301  (step S 305 ). 
     CPU  101  displays the superimposed image  732  created in step S 305  in the image display region  7  in the GUI  2  shown in  FIG. 2  (step S 306 ). Here, it is desired that both the superimposed image  731  onto the panoramic image  71  and the superimposed image  732  onto the virtual endoscopic image  72  are displayed in the image display region  7 . When both the superimposed image  731  onto the panoramic image  71  and the superimposed image  732  onto the virtual endoscopic image  72  are displayed, the comparison of the lesion candidates can be easily performed, and thus reading can be further efficiently performed. 
     When the real space coordinates corresponding to  501   a ,  507   a  in the lesion candidate regions  501   a ,  504   a ,  507   a  in the superimposed image  731  in the panoramic image  71  are within the virtual endoscopic image  72  as shown in  FIG. 21 , the corresponding regions  501   b ,  507   b  are displayed at the corresponding coordinate positions. 
     As described above, according to the third embodiment, the image processing device  100  superimposes and displays the lesion candidate region extracted in the panoramic image  71  at the corresponding position of the virtual endoscopic image  72 . As a result, the comparison reading of the lesion candidate region between the panoramic image  71  and the virtual endoscopic image  72  can be easily performed and thus the diagnosis efficiency is enhanced. 
     As in the case of the first and second embodiments, in the step S 305 , all the lesion candidate regions extracted in step S 301  or only the lesion candidate regions having larger region sizes than a predetermined region size may be set as the lesion candidate regions on which the color values are superimposed. Furthermore, in the third embodiment, the lesion candidate region extracted from the panoramic image  71  is reflected to the virtual endoscopic image  72 . However, conversely, the lesion candidate region extracted from the virtual endoscopic image  72  may be reflected to the panoramic image  71 , or the lesion candidate region extracted from the panoramic image  71  or the virtual endoscopic image  72  may be reflected to the medical tomographic image. 
     Fourth Embodiment 
     In a fourth embodiment, various display styles of the lesion candidate region extracted according to the methods described with reference to the first to third embodiments will be described. 
     As shown in  FIG. 22 , CPU  101  first extracts the lesion candidate region from the panoramic image  71  or the virtual endoscopic image  72  (step S 401 ). The extraction of the lesion candidate region is the same as the processing of the steps S 101  to S 116  of  FIG. 3  or the steps S 201  to S 216  of  FIG. 13 , and the description thereof is omitted. 
     Subsequently, CPU  101  calculates the region size Li for each lesion candidate region i extracted in step S 401  (step S 402 ). The calculation of the region size Li is the same as the step S 109  of  FIG. 3 , the step S 209  of  FIG. 13  or the like. CPU  101  classifies the respective regions i into plural classes such as three stages or the like on the basis of the region size Li calculated in the step S 402  (step S 403 ). 
     CPU  101  creates a superimposed image in the display style (for example, color value, transparency, pattern or the like) corresponding to a class in which each lesion candidate region extracted in step S 401  is classified in step S 403  (step S 404 ), and displays the created superimposed image on the display screen (step S 405 ). 
     As the display style corresponding to the classified class, for example, with respect to lesion candidate regions  501   c ,  504   c ,  507   c  belonging to different classes displayed on the panoramic image  741  and lesion candidate regions  801   c ,  802   c ,  803   c  belonging to different classes displayed on the virtual endoscopic image  742 , the lesion candidate regions having different region sizes are displayed with different colors, for example, like red, blue and yellow as shown in  FIG. 23 . In such a case, an indication such as the degree of risk or the like which is estimated form the region size can be easily determined. 
     Furthermore, as indicated by  501   d ,  504   d ,  507   d ,  801   d ,  802   d ,  803   d  of  FIG. 24 , colors such as red, blue, yellow which are allocated every class may be applied to only the edges of the respective lesion candidate regions. When only the edges of the regions are colored as described above, the surface state of the lesion candidate region can be observed. 
     As indicated by  501   e ,  504   e ,  507   e ,  801   e ,  802   e ,  803   e  of  FIG. 25 , the transparency of coloring may be varied every class. For example, classes in such a level that a lesion candidate region has a large region size and thus overlooking causes a risk is colored with an opaque color so as to make it conspicuous. With respect to classes which have small sizes and thus have a small degree of risk, the transparency is increased or the like. When the transparency is varied every class as described above, more attention is paid to a region having a large size and thus a large degree of risk. 
     As described above, in the image processing system  1  according to the fourth embodiment, the lesion candidate regions extracted from the medical image are classified into plural classes on the basis of the region size, and they are displayed on the medical image in different display styles in accordance with the classified classes. As a result, the degree of risk of lesion can be easily determined on the basis of the difference in display style. 
     In the fourth embodiment, the grouping (classification) based on the region size of the lesion candidate region is executed, however, this embodiment is not limited to this classification. For example, classification based on the form such as the form exponent or the like, or classification based on other feature amounts may be adopted. Furthermore, in the first to fourth embodiments, extraction of a lesion candidate region on the inner wall of a colon has been described. However, not only other hollow organs such as bronchial tubes, blood vessels, small intestine, etc., but also digestive organs such as stomach, etc., prominences at the outside of hollow organs such as aneurysm, etc. may be targeted. 
     The method described with reference to the first to fourth embodiments may be arbitrarily combined. Furthermore, it is apparent that various modifications or alterations may be made by persons skilled in the art within the scope of the technical idea disclosed in this application, and it is understood that they belong to the technical scope of this invention. 
     DESCRIPTION OF REFERENCE NUMERALS 
     
         
           1  image processing system,  100  medical image processing device,  101  CPU  101 ,  102  main memory,  103  storage device,  104  communication IF,  105  display memory,  106  I/F,  107  display device,  108  mouse (external equipment),  109  input device,  110  network,  111  image data base,  112  medical image scanning device,  2  GUI,  4  initial differential distance input frame,  5  initial form exponent threshold value input frame,  6  form exponent threshold value input frame,  7  image display region,  8  size input frame,  9  scroll bar,  71  panoramic image,  715  superimposed image onto panoramic image,  501  to  515  lesion candidate region,  72  virtual endoscopic image,  725  superimposed image onto virtual endoscopic image,  801  to  808  lesion candidate region