Abstract:
An object of this invention is to eliminate the need for any analytic function and maximize the contrast of an image resulting from gray level conversion of an original image. To accomplish this, an image processing apparatus which executes image processing for a radiographical image obtained by converting, into an electrical signal, an intensity distribution of radiation that is radiated to an object and has passed through at least the object, includes a defining unit for defining a gray level conversion curve to be used for gray level conversion on the basis of the contrast of the image resulting from gray level conversion of the original radiographical image, and a gray level conversion unit for converting the gray level of the radiographical image by using the gray level conversion curve defined by the defining unit.

Description:
FIELD OF THE INVENTION 
     The present invention relates to a technique for executing gray level conversion processing for an image and, more particularly, to a technique for executing gray level conversion processing on the basis of the contrast of the image resulting from gray level conversion of the original image. 
     BACKGROUND OF THE INVENTION 
     There is conventionally an imaging method of irradiating an object with radiation and generating an image on the basis of the intensity of the radiation that has passed through the object. In this method, when imaging is executed using an analog film, the dose of X-rays is adjusted such that the observation region has a predetermined density. To the contrary, in digital imaging in which data obtained by a kind of imaging apparatus such as a sensor or camera is displayed on a monitor screen or X-ray diagnostic film, the image density after imaging can arbitrarily-be adjusted by image processing. For this reason, digital data is sometimes acquired by irradiating an object with X-rays in minimum dose, and the gray level of the obtained data is converted to easily observe it (e.g., Japanese Patent Laid-Open No. 2002-245453). In this case, image processing is executed such that the observation region has a predetermined density. An amount that is acquired from digital data and is to be used for the processing is called a feature amount. In, e.g., frontal chest imaging, it is demanded that the fifth intercostal density should be 1.8 to 2.0. The fifth intercostal region is extracted analytically (on the basis of various algorithms) from digital data. A statistical amount (e.g., the average or mode) is calculated from the digital data in that region to obtain a feature amount. Gray level conversion is executed by image processing such that the feature amount (digital value) represents a predetermined density. That is, the feature amount indicates the representative value of the digital data in the observation region or a value that highly correlates to the digital data. To calculate such a feature amount, for example, the two-dimensional structure of an entire object is analyzed to extract a predetermined region, and a feature amount is calculated in the predetermined region. Alternatively, a feature amount is calculated by histogram analysis. 
     However, the analytic function to be used to calculate the above-described feature amount must be prepared for each part of an object, resulting in high development cost. In addition, a portion related to an undeveloped analytic function cannot be imaged. Furthermore, the parameters of a gray level conversion curve must be defined by empirical rules. Conventionally, the contrast is adjusted by trial and error while visually confirming it. There are no objective indicators representing the suitability of the contrast. 
     SUMMARY OF THE INVENTION 
     The present invention enables to provide an image processing apparatus which accurately and stably executes gray level conversion. 
     According to the present invention, the foregoing problem is solved by providing an image processing apparatus which executes image processing for a radiographical image obtained by converting, into an electrical signal, an intensity distribution of radiation that is radiated to an object and has passed through the object, characterized by comprising: 
     defining means for defining a gray level conversion curve to be used for gray level conversion on the basis of a contrast of an image resulting from gray level conversion of the radiographical image; and 
     gray level conversion means for converting a gray level of the radiographical image by using the gray level conversion curve defined by the defining means. 
     Other features and advantages of the present invention will be apparent from the following description taken in conjunction with the accompanying drawings, in which like reference characters designate the same or similar parts throughout the figures thereof. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
         FIG. 1  is a block diagram showing the arrangement of the embodiment of the present invention; 
         FIG. 2  is a flow chart showing the flow of processing in the embodiment; 
         FIG. 3  is a graph showing an example of a gray level conversion curve; and 
         FIG. 4  is a graph showing an example of a contrast improvement factor. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     A preferred embodiment of the present invention will now be described in detail in accordance with the accompanying drawings. It should be noted that the relative arrangement of the components, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise. 
     This embodiment is related to image processing in the medical field to obtain an image having an optimum contrast objectively. The embodiment particularly produces an effect for an image of a bony septum where the contrast is more important than the density. Optimizing the contrast of an entire image will eventually lead to obtaining an image that meets the diagnoser&#39;s needs. It has been empirically indicated that, even in a pulmonary area, setting the fifth intercostal density to 1.8 to 2.0 tends to optimize the contrast of the entire pulmonary area. Conversely, optimizing the contrast of the entire pulmonary area finally results in setting the fifth intercostal density to 1.8 to 2.0. This embodiment assume that the gray level conversion curve of a conventional analog film has a predetermined curve shape. Hence, the curve shape of the analog film is reproduced even for a digital image. 
       FIG. 1  is a block diagram showing the internal arrangement of an X-ray imaging apparatus  100  according to the embodiment of the present invention. The X-ray imaging apparatus  100  is an imaging apparatus having a function of obtaining an image by using X-rays and an image processing function. The X-ray imaging apparatus has a preprocessing circuit  106 , CPU  108 , main memory  109 , operation panel  110 , and image processing circuit  111 . Data transmission between them is done through a CPU bus  107 . 
     The X-ray imaging apparatus  100  also has a data acquisition circuit  105  connected to the preprocessing circuit  106 , and a two-dimensional X-ray sensor  104  and X-ray generation circuit  101 , which are connected to the data acquisition circuit  105 . These circuits are also connected to the CPU bus  107 . 
     In the above-described X-ray imaging apparatus  100 , the main memory  109  stores various kinds of data necessary for processing in the CPU  108 . The main memory  109  also includes a work memory for the work of the CPU  108 . 
     The CPU  108  controls the operation of the entire apparatus by using the main memory  109  in accordance with an operation from the operation panel  110 . Accordingly, the X-ray imaging apparatus  100  operates in the following way. 
     First, the X-ray generation circuit  101  radiates an X-ray beam  102  to an object  103 . 
     The X-ray beam  102  radiated from the X-ray generation circuit  101  passes through the object  103  while attenuating, arrives at the two-dimensional X-ray sensor  104 , and is output as an X-ray image from the two-dimensional X-ray sensor  104 . In this example, the X-ray image output from the two-dimensional X-ray sensor  104  is, e.g., a human body part image such as a thoracic spine image. 
     The data acquisition circuit  105  converts the X-ray image output from the two-dimensional X-ray sensor  104  into an electrical signal and supplies it to the preprocessing circuit  106 . The preprocessing circuit  106  executes preprocessing such as offset correction processing and gain correction processing for the signal (X-ray image signal) from the data acquisition circuit  105 . The X-ray image signal preprocessed by the preprocessing circuit  106  is transferred to the main memory  109  and image processing circuit  111  through the CPU bus  107  as an original image under the control of the CPU  108 . 
     Reference numeral  111  indicates the arrangement of the image processing circuit. The image processing circuit  111  includes an irradiation field recognition circuit  112 , object extraction circuit  113 , analyzing circuit  114 , and gray level conversion circuit  115 . The irradiation field recognition circuit  112  extracts a region where the two-dimensional X-ray sensor  104  is directly irradiated with X-rays. The object extraction circuit  113  deletes a transparent region in the irradiation region extracted by the irradiation field recognition circuit  112  and a body region that is in contact with the transparent region in a predetermined width, thereby extracting the object. The analyzing circuit  114  defines a gray level conversion curve, with which the contrast of an image resulting from gray level conversion is maximized. The gray level conversion curve is defined on the basis of the contrast of the image resulting from gray level conversion of the original image. The gray level conversion circuit  115  converts the gray level of the original image on the basis of the gray level conversion curve defined by the analyzing circuit  114 . 
     The operation of the image processing circuit  111  will be described next with reference to  FIG. 2 .  FIG. 2  is a flow chart showing the flow of processing in the embodiment. 
     The irradiation field recognition circuit  112  receives the input image processed by the preprocessing circuit  106  through the CPU bus  107  under the control of the CPU  108  and extracts the irradiation region in the input image (step S 201 ). The object extraction circuit  113  replaces a region outside the irradiation region extracted by the irradiation field recognition circuit  112 , a transparent region in the irradiation region, and a body region that is in contact with the transparent region in a predetermined distance with, e.g., 0 pixels to extract the object (step S 202 ). More specifically, the image is converted by 
                     f1   ⁡     (     x   ,   y     )       =       f   ⁡     (     x   ,   y     )       ×       ∏     x1   =     -   d1         x1   =   d1       ⁢           ⁢       ∏     y1   =     -   d2         y1   =   d2       ⁢     sgn   ⁡     (       x   +   x1     ,     y   +   y1       )                     (   1   )               
where f(x, y) indicates image data, and f 1 (x, y) indicates the image after the transparent region and the body region in contact with the transparent region in a predetermined distance are deleted. Here, sgn(x, y) is given by
 
                             sgn   ⁡     (     x   ,   y     )       =   0             when   ⁢           ⁢     f   ⁡     (     x   ,   y     )         ≥   Th1                 sgn   ⁡     (     x   ,   y     )       =   1         otherwise         }           (   2   )               
where Th 1  is a constant defined by experiments, which takes, e.g., a value 90% of the maximum pixel value of the entire image, and d 1  and d 2  are constants that define the width of deletion of the body region.
 
     A gray level conversion curve F(d, c)(x) is given by, e.g.,
 
 F ( d,c )( x )=Dmin+{(Dmax−Dmin)/2}[[1/{1+exp( c ( x 0−( x−d )))}]+[1/{1+exp( a×c ( b×x 0−( x−d )))}]]  (3)
 
where d is a parameter that indicates the translation amount of the gray level conversion curve with respect to the pixel value, and c is the contrast of the gray level conversion curve, which indicates the tilt amount of the gray level conversion curve. When the parameter d changes, the gray level conversion curve is translated with respect to the pixel value. When the contrast c changes, the tilt of the gray level conversion curve changes. In addition, Dmax is a constant corresponding not the maximum density value, Dmin is a constant corresponding to the minimum density value, and x0,  a , and b are constants. When the image is displayed on a monitor, Dmax and Dmin are changed in correspondence with the monitor so that they can cope with luminance values. By this gray level conversion curve, the pixel value f 1 (x, y) is converted into the density value F(d, c)(f 1 (x, y)) after gray level conversion.
 
       FIG. 3  is a graph showing an example of a gray level conversion curve  301 . The contrast of the gray level conversion curve is fixed, and the gray level conversion curve is moved horizontally with respect to the pixel value. 
     The difference in contrast before and after gray level conversion is controlled by the tilt of the gray level conversion. For example, when the tilt is 0, the image has no contrast. If the tilt is infinite, the contrast is also infinite. Here, the tilt of the gray level conversion curve will be referred to as the “contrast of the gray level conversion curve”. 
     The analyzing circuit  114  calculates a contrast improvement factor C(d) given by 
                     C   ⁡     (   d   )       =       ∫   dy     ⁢       ∫   dx     ⁢         F   (           ⁢     ⅆ     ,   c       )     ′     ⁢     (     f1   ⁡     (     x   ,   y     )       )     ⁢     ⅆ   x     ⁢       ⅆ   y     /       ∫   dy     ⁢       ∫   dx     ⁢       Sgn   ⁡     (     f1   ⁡     (     x   ,   y     )       )       ⁢     ⅆ   x     ⁢     ⅆ   y                           (   4   )               
where F(d, c)′( ) indicates the differential value of the gray level conversion curve, i.e., the contrast.
 
     From equation (4), a contrast improvement factor C(d) of the object for a parameter d is calculated (S 203 ).  FIG. 4  shows an example of the contrast improvement factor of the image after gray level conversion. This contrast improvement factor plainly indicates the improvement in contrast from the entire image before gray level conversion. The larger this value becomes, the higher the contrast of the entire image becomes. 
     The parameter d of the contrast improvement factor C(d) defined by equation (4) is changed within a predetermined range, and the contrast improvement factor C(d) in that range is calculated. The parameter d when the contrast improvement factor C(d) indicates the maximum value is decided as the optimum parameter D. Accordingly, the contrast of the entire image indicates the maximum value. The predetermined range of the parameter d is empirically defined in advance on the basis of the gray level conversion curve. 
     The analyzing circuit  114  calculates a parameter D with which the contrast improvement factor C(d) is maximized (S 204 ). With this processing, the gray level conversion curve is defined, with which the contrast improvement factor of the image after gray level conversion of the entire object is maximized. In medical diagnosis, the diagnosis capability can sometimes be increased by fixing the contrast of gray level conversion and diagnosing the image of each object. Hence, the contrast of the gray level conversion curve itself is fixed. 
     Similarly, a parameter with which the contrast improvement factor is maximized can be decided by changing the parameter c. In this case, the tilt of the gray level conversion curve itself can also be adjusted. Hence, the contrast improvement factor can be further increased. When the parameters d and c are simultaneously changed to calculate optimum parameters D and C, an optimum gray level curve shape can be decided. 
     The gray level conversion circuit  115  executes gray level conversion processing of the image f(x, y) by using the gray level conversion curve F(D, C)( ) defined by the parameter D, C calculated by the analyzing circuit  114  (S 205 ). 
     In calculating the contrast improvement factor, the contrast in a specific pixel value range (e.g., a region corresponding to the pulmonary area) may be calculated. In a human body image, the region to be diagnosed is limited. For example, a frontal chest image is mainly used to diagnose the pulmonary area and not the belly part. Hence, the contrast of the region to be diagnosed is preferably maximized. For example, when the contrast improvement factor is limited to that of the pulmonary area, the contrast of the region as the principal target can efficiently be increased. 
     Alternatively, an anatomical region may be two-dimensionally calculated, and the contrast improvement factor only in that two-dimensional region may be calculated. In this case, the contrast of the specific region can be more efficiently increased. 
     According to this embodiment, an image processing apparatus which accurately and stably executes gray level conversion can be obtained. When the contrast improvement factor that is defined by the shape of the gray level conversion curve is calculated, the contrast of a target image after gray level conversion can be calculated. The gray level conversion curve can be defined by using the value of the contrast improvement factor as an indicator. For this reason, the analytic function for each part need not be developed. In addition, gray level conversion can efficiently be executed such that the contrast of the image after gray level conversion is maximized. When the contrast of the entire object is increased, the diagnosis capability can be eventually increased. 
     In the medical field, the tilt of a gray level conversion curve, i.e., the contrast of a gray level conversion curve tends to be fixed. If the tilt of the gray level conversion curve changes for each imaging object, the gray level of the image may be felt differently, and the diagnosis standard may change. 
     However, when the contrast of the gray level conversion curve, i.e., the tilt of the gray level conversion curve is fixed, and the contrast improvement factor is calculated by translating the gray level conversion curve, the above-described requirement can be satisfied. 
     When the contrast of the gray level conversion curve itself can be changed as a parameter, the contrast improvement factor can further be increased. 
     In calculating the contrast improvement factor, when the contrast of a specific image region (e.g., a region corresponding to the pulmonary area) is calculated, the contrast of the region (specific image region) to be diagnosed is maximized. 
     When an anatomical region is two-dimensionally calculated, and the contrast improvement factor only in that two-dimensional region is calculated, the contrast of the specific region can be more efficiently increased. 
     (Other Embodiment) 
     The embodiment of the present invention has been described above in detail. The present invention can be applied to a system constituted by a plurality of devices, or to an apparatus comprising a single device. 
     The present invention is also achieved by supplying a software program which implements the function of the above-described embodiment to the system or apparatus directly or from a remote site and causing the computer of the system or apparatus to read out and execute the supplied program code. The form need not always be a program as long as the functions of the program can be obtained. 
     Hence, to implement the functional processing of the present invention by a computer, the program code itself, which is installed in the computer, also implements the present invention. That is, a computer program itself, which implements the functional processing of the present invention, is also incorporated in the claim of the present invention. 
     In this case, the program can take any form such as an object code, a program to be executed by an interpreter, or script data to be supplied to the OS as long as the functions of the program can be obtained. 
     As a recording medium for supplying the program, for example, a floppy (registered trademark) disk, hard disk, optical disk, magnetooptical disk, MO, CD-ROM, CD-R, CD-RW, magnetic tape, nonvolatile memory card, ROM, or DVD (DVD-ROM or DVD-R) can be used. 
     As another program supply method, a client computer may be connected to a homepage on the Internet using a browser in the computer, and the computer program itself of the present invention or a compressed file containing an automatic install function may be downloaded from the homepage to a recording medium such as a hard disk. A program code that constitutes the program of the present invention may be divided into a plurality of files, and the files may be downloaded from different homepages. That is, a WWW server which causes a plurality of users to download a program file that causes a computer to implement the functional processing of the present invention is also incorporated in the claim of the present invention. 
     The program of the present invention may be encrypted, stored in a storage medium such as a CD-ROM and distributed to users. Any user who satisfies predetermined conditions may be allowed to download key information for decryption from a homepage through the Internet, execute the encrypted program using the key information, and install the program in the computer. 
     The function of the above-described embodiment is implemented not only when the readout program is executed by the computer but also when the OS or the like, which is running on the computer, performs part or all of actual processing on the basis of the instructions of the program. 
     The function of the above-described embodiment is also implemented when the program read out from the storage medium is written in the memory of a function expansion board inserted into the computer or a function expansion unit connected to the computer, and the CPU of the function expansion board or function expansion unit performs part or all of actual processing on the basis of the instructions of the program. 
     As many apparently widely different embodiments of the present invention can be made without departing from the spirit and scope thereof, it is to be understood that the invention is not limited to the specific embodiments thereof except as defined in the appended claims.