Patent Publication Number: US-8126288-B2

Title: Image processing apparatus

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from prior Japanese Patent Applications No. 2007-022110, filed Jan. 31, 2007; and No. 2008-002463, filed Jan. 9, 2008, the entire contents of both of which are incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention is applied to digital images. In particular, the present invention relates to an image processing apparatus applied to noise reduction processing for images in which a ratio of signal components to noise components (S/N ratio) locally varies, typically medical diagnostic images such as nuclear medicine images, CT images, and MRI images. 
     2. Description of the Related Art 
     Noise reduction in digital images has been performed by cutting off (eliminating) high-frequency components by high-frequency cutoff filters, such as Butterworth filters and Gauss filters. However, since the same high-frequency cutoff processing is performed through the whole images, there is caused a problem that part of information is deteriorated in medical diagnostic images such as nuclear medicine images, CT images, and MRI images, although it causes no problem in general digital images (such as landscape images taken by digital cameras). 
     The first cause of this problem is that a ratio of signal components to noise components (S/N ratio) locally varies from position to position (from pixel to pixel in minimum unit) in medical diagnostic images. This is because the S/N ratio serving as a total noise of a position varies according to collection counts obtained from each pixel in medical diagnostic images. Therefore, when the same high-frequency component cutting processing is performed through the whole image, overcorrected parts and insufficiently corrected parts are generated according to positions. As a result, the processed image includes regions having deteriorated information (such as spatial resolution and contrast) (overcorrection) and regions in which noise reduction is insufficient (insufficient correction). 
     The second cause of the problem is that medical diagnostic images have a rough (large) pixel size. For example, spatial resolution of nuclear medicine images is about 10 mm, and the pixel size of images expressing it is several millimeters. When filtering is performed by Fourier transform, there are cases where sufficient sampling is not performed and artifacts are generated, since the pixel size is large. Refer to Jpn. Pat. Appln. KOKAI Pub. No. 2001-59872. 
     BRIEF SUMMARY OF THE INVENTION 
     An object of the present invention is to achieve relatively uniform noise reduction processing for images in which the S/N ratio locally varies, or achieve elimination of artifacts caused by noise reduction processing for images having a relatively large pixel size, in noise reduction processing caused by properties of medical diagnostic digital images. 
     According to an aspect of the present invention, there is provided an image processing apparatus comprising: a storing section which stores data of a digital image; a rotation processing section which generates a plurality of rotated digital images having different rotation angles from the digital image; an image processing section which generates a plurality of image-processed digital images from the rotated digital images; a reverse processing section which generates a plurality of reversed digital images from the image-processed digital images; and a combining section which combines the reversed digital images into one digital image. 
     Additional objects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objects and advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out hereinafter. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING 
       The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention, and together with the general description given above and the detailed description of the embodiments given below, serve to explain the principles of the invention. 
         FIG. 1  is a diagram illustrating a structure of an image processing apparatus according to an embodiment of the present invention. 
         FIG. 2  is a flowchart illustrating a process of noise reduction processing according to the embodiment. 
         FIG. 3  is a conceptual illustration of wavelet transform. 
         FIG. 4  is an explanatory diagram illustrating calculation processing of a local cutoff frequency of  FIG. 2 . 
         FIG. 5A  is a diagram illustrating the former stage of the calculation processing of the cutoff frequency illustrated in  FIG. 2 . 
         FIG. 5B  is a diagram illustrating the latter stage of the calculation processing of the cutoff frequency illustrated in  FIG. 2 . 
         FIG. 6  is a schematic diagram illustrating a process of the noise reduction processing according to the embodiment. 
         FIG. 7A  is an explanatory diagram of image rotation processing of  FIG. 3 . 
         FIG. 7B  is an explanatory diagram of another image rotation processing of  FIG. 3 . 
         FIG. 8A  is a diagram illustrating an example of a phantom image as input data of  FIG. 2 . 
         FIG. 8B  is a diagram illustrating an example of the phantom image subjected to conventional filtering. 
         FIG. 8C  is a diagram illustrating an example of the phantom image subjected to filtering according to the embodiment. 
         FIG. 9A  is a diagram illustrating an example of a SPECT clinical image subjected to conventional filtering. 
         FIG. 9B  is a diagram illustrating an example of the SPECT clinical image subjected to filtering according to the embodiment. 
         FIG. 10A  is a diagram illustrating an example of a phantom image obtained by CT with high current tube as input data of  FIG. 2 . 
         FIG. 10B  is a diagram illustrating an example of another phantom image obtained by CT with low current tube as input data of  FIG. 2 . 
         FIG. 10C  is a diagram illustrating an example of the phantom image obtained by CT and subjected to conventional filtering. 
         FIG. 10D  is a diagram illustrating an example of the phantom image obtained by CT and subjected to filtering according to the embodiment. 
         FIG. 11  is a diagram illustrating a standard angle of a rotation pitch of the embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     An embodiment of an image processing apparatus according to the present invention is explained below with reference to drawings. 
     An image processing apparatus according to the embodiment is connected, through an interface  10 , to external apparatuses which store or generate data of digital medical images, such as PACS, an X-ray computerized tomography apparatus (CT), a magnetic resonance imaging apparatus (MRI), and an X-ray diagnostic apparatus. An image storing section  13  is provided to store digital image data to be subjected to image processing, which are received from these external apparatuses through the interface  10 . A control section  11  which controls operation of the whole apparatus is connected with the interface  10  and the image storing section  13 , together with an image processing section  15 , a cutoff frequency calculating section  17 , a wavelet transform processing section  19 , and a filtering section  21 , via a data/control bus  12 . 
     The image processing section  15  has a function of subjecting digital images to be image-processed to rotation processing according to a rotation angle instructed from the control section  11 , and a function of generating a final filtered digital image by averaging a plurality of digital images filtered by the filtering section  21  according to instructions from the control section  11 . The cutoff frequency calculating section  17  calculates a cutoff frequency for each local region of the digital image, on the basis of a standard deviation of each local region, as described below. 
     The wavelet transform processing section  19  performs wavelet transform processing for digital image data to be image-processed. The wavelet transform processing is processing for expressing a digital image in a frequency space while maintaining spatial information of the original digital image. The filtering section  21  cuts off high-frequency components exceeding the cutoff frequency calculated for each local region by the cutoff frequency calculating section  17 , for each local region of the digital image subjected to wavelet transform processing. The filtered digital image is returned to the original actual spatial region by inverse wavelet transform by the wavelet transform processing section  19 . A plurality of digital images having subjected to rotation processing with different angles and passed through the filter are subjected to averaging by the image processing section  15 . 
     The above wavelet transform processing can be replaced by Fourier transform which belongs to the same category of frequency analysis processing. 
     Further, although the above explanation provides that the image processing includes rotation and averaging, the meaning of the term “image processing” is not limited to the above. The term “image processing” has a broad meaning of including at least one of rotation, averaging, frequency analysis, and filtering, etc. 
     First, an outline of wavelet transform processing is explained. The principle of wavelet transform is defined by the following expression, as widely known. 
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     ψ(t): analyzing wavelet (wavelet) 
     ω: wavelet expansion series 
     f(t): object signal 
     j: level (scaling) 
     k: shift (parallel displacement) 
     In the embodiment, as illustrated in  FIG. 3 , wavelet transform is two-dimensional processing since the object of the processing is an image. As widely known, all elements (components) are transformed into frequency components in Fourier transform, and therefore spatial information is lost. However, wavelet transform can express a digital image in a frequency space with spatial information maintained. For example, the original image is divided into a low-frequency vertical component, a low-frequency diagonal component, a high-frequency vertical component, and a high-frequency diagonal component by two two-dimensional wavelet transforms, and the divided components are displayed. 
       FIG. 2  illustrates a filtering process according to the embodiment. First, in step S 10 , the cutoff frequency calculating section  17  calculates “cutoff frequency for each local region” used for noise reduction processing (filtering) for digital image subjected to wavelet transform, for the input data (original digital image data). 
       FIGS. 5A and 5B  illustrate cutoff frequency calculation processing. In noise reduction in conventional wavelet transform, one cutoff frequency is applied to the whole image, without an idea of dividing the image into local regions. A typical example thereof is a method by Donoho, in which a cutoff frequency is determined from a standard deviation of the whole image. In the present embodiment, local regions are determined to obtain local information, and an index indicating the S/N ratio in each local region is determined. In this example, a square having sides of several pixels is adopted as a local region. A cutoff frequency of a local region is determined on the basis of the value C.V. of coefficient of variation in the local region. Processing in local regions is performed through the whole image, and thereby cutoff frequencies for respective local regions in the whole image can be determined. 
     Specifically, a plurality of local regions are set for the original digital image. A cutoff frequency Z is individually calculated for each of the local regions, based on a standard deviation SD for the local region. More specifically, the calculation is indicated by the following expression.
 
 Z=f (CV)×Coef
 
     CV: Coefficient of variation 
     Coef: Coefficient 
     f(CV): SD×(2×ln(n)) 
     n: number of pixels in the local region 
     Return to  FIG. 3 . In step S 11 , the original digital image to be processed is subjected to rotation processing with the image center thereof serving as the center, in the image processing section  15 . The image is rotated by a designated angle, and subjected to wavelet transform, filtering (noise reduction processing), and inverse wavelet transform. Lastly, the image is inversely rotated by the same designated angle, and returned to the original angle. This processing is performed for each angle being an integral multiple of the designated angle between 0 to 360°. Since the object image is a square matrix, a rotation of 90° can be regarded as being equal to a rotation of 360°. An average image is generated by using all the images processed at the respective angles. This processing enables elimination of artifacts generated in noise reduction processing in images of a large pixel size. 
     The rotation processing may be rotating the image with the coordinate system fixed as illustrated in  FIG. 7A , or may be rotating the coordinate axes in wavelet transform as illustrated in  FIG. 7B . The rotation pitch is typically set to 5° as illustrated in  FIG. 6 . The rotation pitch R is set to any angle selected from a range of 0&lt;R≦45°. More preferably, the rotation pitch R is set to about 5° (3≦R≦10°). 
     Further, a standard angle θ of the rotation pitch R is defined as follows. The rotation pitch R is preferably set to n×θ. As illustrated in  FIG. 11 , n is a positive integer, and has a value which is ½ or less the number of pixels M parallel to the X axis. The value of n should typically be set to 3 or 5, in consideration of balance between the processing amount and the processing effect which have a trade-off relationship.
 
tan θ= L 2/ L 1
 
     L1: Distance between the center of the image and an edge of the utmost end pixel on the Y axis 
     L2: Length of a single pixel 
     In the first processing, the rotation angle is 0, that is, the digital image is not rotated. A rotated digital image is subjected to wavelet transform processing in the wavelet transform processing section  19  (step S 12 ). 
     The local regions of the digital image subjected to wavelet transform processing are subjected to filtering by the filtering section  21  by using the respective cutoff frequencies calculated for the respective local regions in step S 10  (step S 13 ). By the filtering, high-frequency components exceeding the cutoff frequencies individually calculated for the respective local regions are eliminated. 
     Actually, as illustrated in  FIGS. 4 and 7 , wavelet images (ω images) of three components, that is, the high-frequency vertical component, the high-frequency horizontal component, and the high-frequency diagonal component, obtained by wavelet transform processing, are added. Then, the local regions in the added image are subjected to filtering by the filtering section  21 , by using the respective cut-off frequencies calculated for the respective local regions in step S 10 . The ratio of pixel value between the added image before processing to the added image after processing is multiplied by the wavelet images of the three components, and the components are rearranged in their original positions. 
     In  FIG. 3  again, the digital image subjected to filtering is subjected to inverse wavelet transform processing in the wavelet transform processing section  19  (step S 14 ), and returned to the original actual space region. Then, in step S 15 , the digital image is rotated in the reverse direction by the same angle as that used in step S 1 , and returned to the initial angle. 
     The loop from steps S 11  to S 15  is repeated predetermined times with the rotation angle in 5-degree increments. Thereby, a plurality of filtered digital images are generated with the rotation angle in 5-degree increments. The filtered digital images are subjected to averaging in the image processing section  15  (step S 16 ). 
       FIG. 8A  illustrates an example of a phantom image as the input data of  FIG. 2 , to be compared with the phantom image subjected to conventional filtering illustrated in  FIG. 8B , and the phantom image subjected to filtering according to the present embodiment illustrated in  FIG. 8C .  FIG. 9A  illustrates an example of a SPECT (nuclear medicine) clinical image subjected to conventional filtering, and  FIG. 9B  illustrates an example of the clinical image subjected to filtering according to the present embodiment.  FIGS. 10A and 10B  illustrate phantom images obtained by CT,  FIG. 10D  illustrates an example of the phantom image obtained by CT and subjected to filtering according to the present embodiment, and  FIG. 10C  illustrates an example of the CT phantom image subjected to conventional filtering. 
     As is clear from comparison of the examples of images, according to the present embodiment, it is possible to achieve relatively uniform noise reduction processing for images in which the S/N ratio locally varies, and eliminate artifacts generated by noise reduction processing for images having a relatively large pixel size. 
     Additional advantages and modifications will readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described herein. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents.