Abstract:
At least one of the common regions in time-sequence images is specified. Then, the time-sequence images are adjusted such that the position of that common region in at least one image among the time-sequence images is adjusted to the position of that common region in a different image. Then, the new adjusted time-sequence images are subjected to video compression processing.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY CLAIM 
       [0001]    This application is a Continuation of International Application No. PCT/JP2012/075332 filed on Oct. 1, 2012, which was published under PCT Article 21(2) in Japanese, which is based upon and claims the benefit of priority from Japanese Patent Application No. 2012-068698 filed on Mar. 26, 2012, the contents all of which are incorporated herein by reference. 
     
    
     TECHNICAL FIELD 
       [0002]    The present invention relates to an image processing apparatus, an image processing method, and a non-transitory storage medium. 
       BACKGROUND ART 
       [0003]    Recently, in the medical field, due to the rapid development of high-performance image capturing apparatus (or modality) and dose reduction technology, the number of medical images that can be acquired per each examination process has been increasing. However, since such an increased number of acquired medical images results in a vast amount of data that must be handled by an overall medical diagnostic system, problems arise such as a sharp increase in the cost of data storage and transmission. It is effective to reduce the amount of data to be handled by performing an image compression process on the medical images before the medical images are transmitted or saved (see Japanese Laid-Open Patent Publication No. 2003-126046). Various technologies for increasing the efficiency with which images are compressed have been proposed in the art. 
         [0004]    According to Japanese Laid-Open Patent Publication No. 2009-291280, there have been proposed a system for and a method of sorting a sequence of cross-sectional images along a direction in which pixels of the images change. Then, the sorted images are regarded as a moving image, and an image compression process is performed thereon. 
         [0005]    According to Japanese Laid-Open Patent Publication No. 2005-245922, a method and an apparatus have been proposed in which blocks of image data concerning pixels that share positions are encoded, and thereafter, the encoded blocks are sorted into groups in order to compress the image data. 
       SUMMARY OF INVENTION 
       [0006]    The methods, etc., disclosed in Japanese Laid-Open Patent Publication No. 2009-291280 and Japanese Laid-Open Patent Publication No. 2005-245922 attempt to increase compression efficiency by focusing on a change in an image between adjacent frames, and sorting (or grouping) cross-sectional images in order to reduce the amount of change. However, in case the structure of a subject that has been imaged is known, or in case a time-dependent change in the structure is comparatively small, there is sufficient room for further increasing the efficiency with which a time series of images representing the structure can be compressed. 
         [0007]    The present invention has been made in order to solve the aforementioned problems. An object of the present invention is to provide an image processing apparatus, an image processing method, and a storage medium, which are capable of further increasing the efficiency with which a time series of images can be compressed. 
         [0008]    According to the present invention, there is provided an image processing apparatus comprising a time-series image acquirer for acquiring time-series images made up of a plurality of raw images generated in a time series, a common region designator for designating at least one common region that is commonly included in the time-series images acquired by the time-series image acquirer, a positioning processor for adjusting the time-series images by bringing a common region that is represented by at least one raw image of the time-series images into positional alignment with a common region that is represented by another raw image, and an image compression processor for performing a moving-image compression process on new time-series images adjusted by the positioning processor. 
         [0009]    Since the image processing apparatus includes the positioning processor, which brings a common region that is represented by at least one raw image of the time-series images into positional alignment with a common region that is represented by another raw image, at least in the common region, it is possible to generate time-series images having a small interframe image change. Further, in a case where the adjusted new time-series images are compressed, the time-series images can be compressed with increased efficiency. 
         [0010]    Preferably, the time-series image acquirer acquires, as the time-series images, a time series of cross-sectional image groups, which are generated by capturing slice images along an axial direction, and the positioning processor adjusts the time-series images by positioning the common region along the axial direction. 
         [0011]    Preferably, the positioning processor adjusts the time-series images by using the position of the common region, which is represented by one raw image of the time-series images as a reference, and bringing a common region that is represented by remaining raw images into positional alignment with the reference. 
         [0012]    Preferably, the positioning processor adjusts the time-series images by positioning the common region along a cross-sectional direction normal to the axial direction. 
         [0013]    Preferably, the time-series image acquirer acquires, as the time-series images, a cross-sectional image group generated by capturing slice images along an axial direction, and the positioning processor adjusts the time-series images by positioning the common region along a cross-sectional direction normal to the axial direction. 
         [0014]    Preferably, the positioning processor adjusts the time-series images by bringing the common region, which is represented by each of the raw images, into positional alignment with the common region that is represented by an adjacent raw image in a time series. 
         [0015]    Preferably, the common region designator designates, as the common region, at least one of a lung, a liver, a heart, a spleen, a kidney, a head, and a body. 
         [0016]    According to the present invention, there also is provided an image processing method comprising an acquiring step of acquiring time-series images made up of a plurality of raw images generated in a time series, a designating step of designating at least one common region that is commonly included in the acquired time-series images, a positioning step of adjusting the time-series images by bringing a common region that is represented by at least one raw image of the time-series images into positional alignment with a common region that is represented by another raw image, and a compressing step of performing a moving-image compression process on adjusted new time-series images. 
         [0017]    Preferably, the positioning step further acquires positioning data for positioning the common regions, the image processing method further comprising a storing step of storing compressed data generated by the compressing step, the compressed data being associated with the positioning data. 
         [0018]    According to the present invention, there further is provided a non-transitory computer-readable recording medium storing a program therein, the program causing a computer to carry out the steps of acquiring time-series images made up of a plurality of raw images generated in a time series, designating at least one common region that is commonly included in the acquired time-series images, adjusting the time-series images by bringing a common region that is represented by at least one raw image of the time-series images into positional alignment with a common region that is represented by another raw image, and performing a moving-image compression process on adjusted new time-series images. 
         [0019]    With the image processing apparatus, the image processing method, and the recording medium according to the present invention, inasmuch as a common region that is represented by at least one raw image of the time-series images is brought into positional alignment with a common region that is represented by another raw image, at least in the common regions, it is possible to generate time-series images having a small interframe image change. Further, in a case where the adjusted new time-series images are compressed, the time-series images can be compressed with increased efficiency. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0020]      FIG. 1  is a schematic view of a medical diagnostic system incorporating an image processing apparatus according to first and second embodiments of the present invention; 
           [0021]      FIG. 2  is an electric block diagram of the image processing apparatus shown in  FIG. 1 ; 
           [0022]      FIG. 3  is a first flowchart of an operation sequence of the image processing apparatus shown in  FIGS. 1 and 2 ; 
           [0023]      FIG. 4  is a schematic view showing by way of example a cross-sectional image group representing a chest of a subject; 
           [0024]      FIGS. 5A through 5C  are schematic views showing a positional relationship between a human body image and image areas that are represented by respective cross-sectional image groups; 
           [0025]      FIGS. 6A through 6C  are schematic views illustrating a positioning process for the cross-sectional image groups; 
           [0026]      FIG. 7  is a second flowchart of the operation sequence of the image processing apparatus shown in  FIGS. 1 and 2 ; 
           [0027]      FIG. 8  is an enlarged partial view of the cross-sectional image group shown in  FIG. 4 ; and 
           [0028]      FIG. 9  is an enlarged partial view of a new cross-sectional image group, which is generated after the positioning process has been performed on the cross-sectional image group shown in  FIG. 8 . 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0029]    Image processing methods according to preferred embodiments of the present invention, in relation to an image processing apparatus and a recording medium (program) for carrying out the image processing methods, will be described below with reference to the accompanying drawings. 
       System Arrangement Common to the Embodiments 
       [0030]      FIG. 1  is a schematic view of a medical diagnostic system  10 , which incorporates therein an image processing apparatus  26  according to first and second embodiments of the present invention. 
         [0031]    The medical diagnostic system  10  basically includes a modality  14  for generating a medical image of a subject  12 , an image server  16  for saving and managing medical images, and an image diagnosing apparatus  18  for displaying medical images for diagnosis or the like. The modality  14 , the image server  16 , and the image diagnosing apparatus  18  are connected to each other over a network  20 . 
         [0032]    The modality  14  captures an image of the subject  12  to thereby generate a medical image for diagnosis. The modality  14  outputs the generated medical image and ancillary information (image capturing conditions and information concerning the subject  12 , etc.) to the image server  16 . In the illustrated example, the modality  14  comprises a CR (Computed Radiography) apparatus  22  and an X-ray CT (Computed Tomography) apparatus  24 . However, the modality  14  is not limited to such an apparatus configuration, and may consist of a combination of other types of image generating apparatus including a DR (Digital Radiography) apparatus, an MRI (Magnetic Resonance Imaging) apparatus, an ultrasonic (US) diagnosing apparatus, and a PET (Positron Emission Tomography) apparatus, etc., for example. 
         [0033]    The image server  16  includes an image processing apparatus  26 , which is constituted by a computer for performing a desired image processing process on the medical image supplied from the modality  14 , and a storage device  28  for saving and managing the medical image that is processed by the image processing apparatus  26 . 
         [0034]      FIG. 2  is an electric block diagram of the image processing apparatus  26  shown in  FIG. 1 . The image processing apparatus  26  includes a communication I/F  30 , a memory  32  (storage medium), and a controller  34 . 
         [0035]    The communication I/F  30  comprises an interface for sending electric signals to and receiving electric signals from an external apparatus. The image processing apparatus  26  acquires a medical image, which is saved and managed by the storage device  28  (see  FIG. 1 ) through the communication I/F  30 . The medical image may be a two-dimensional image or a three-dimensional image. More specifically, the medical image may comprise a radiographic image (a so-called non-contrast radiographic image), a CT image, an MRI image, a PET image, or the like. 
         [0036]    The memory  32  stores programs and data required for the controller  34  to control various components. The memory  32  may comprise a computer-readable non-transitory storage medium, such as a nonvolatile memory, a hard disk, or the like. As shown in  FIG. 2 , the memory  32  is illustrated as being capable of storing a time-series cross-sectional image group  36  including three cross-sectional image groups  36   a ,  36   b , and  36   c , time-series cross-sectional images  38 , positioning data  40 , and compressed data  42 . Plural raw images (the time-series cross-sectional image group  36 , the time-series cross-sectional images  38 ), which are captured time series of images of the same subject  12 , are referred to collectively as “time-series images  44 ”. 
         [0037]    The controller  34  comprises an information processor such as a CPU (Central Processing Unit) or the like. In a case where the controller  34  reads and executes programs that are stored in the memory  32 , the controller  34  functions as a time-series image acquirer  46  that acquires time-series images  44 , a region recognizer  48  that recognizes a region (tissue) of the subject  12  that is included in the time-series images  44 , a common region designator  50  that designates at least one region (hereinafter referred to as a “common region”) that is commonly included in the time-series images  44 , a positioning processor  52  that aligns the position of a common region, which is represented by at least one raw image of the time-series images  44 , with the position of another common region, which is represented by another raw image, and an image compression processor  54  that performs a moving-image compression process on time-series images (new time-series images) that have been positioned by the positioning processor  52 . 
         [0038]    The image processing apparatus  26  according to the first and second embodiments are configured as described above. Operations of the image processing apparatus  26  will be described in detail below primarily with reference to flowcharts shown in  FIGS. 3 and 7 . 
       First Embodiment 
       [0039]    A first embodiment for performing an image compression process on a time series of cross-sectional image groups, which are captured and generated at different times, will be described below with reference to  FIGS. 3 through 6C . 
         [0040]    In step S 1  of  FIG. 3 , the image processing apparatus  26  acquires a time-series cross-sectional image group  36 , which represents a time series of cross-sectional image groups, through the communication I/F  30 . More specifically, the image processing apparatus  26  acquires a time-series cross-sectional image group  36 , which is obtained by capturing images of the same subject  12 . The captured images are saved as medical images in the storage device  28 . 
         [0041]      FIG. 4  is a schematic view showing by way of example a cross-sectional image group  36   a  that represents a chest of the subject  12 . The cross-sectional image group  36   a  is made up of raw images Io(1) through Io(N) (where, for example, N=15 as shown in  FIG. 4 ), in which the raw images make up a plurality of cross-sectional images parallel to an X-Y plane (each of an X-axis and a Y-axis). The cross-sectional images are arranged at equal intervals along a Z-axis (predetermined axis). According to the first embodiment, for constituting the time-series cross-sectional image group  36 , it is assumed that the image processing apparatus  26  acquires a cross-sectional image group  36   a  in a first image capturing process, another cross-sectional image group  36   b  in a second image capturing process, and another cross-sectional image group  36   c  in a third image capturing process (see  FIG. 2 ). The cross sectional image groups  36   a  through  36   c  are stored temporarily in the memory  32 . The time-series cross-sectional image group  36  is not limited to three cross-sectional image groups, and the number of groups is insignificant. 
         [0042]      FIGS. 5A through 5C  are schematic views showing a positional relationship between a human body image  60  and image areas  58   a  through  58   c  represented by the respective cross-sectional image groups  36   a  through  36   c.    
         [0043]    As shown in  FIG. 5A , the cross-sectional image group  36   a  makes up a three-dimensional image area  58   a . The image area  58   a  includes a human body image  60  of the subject  12  (see  FIG. 1 ), which is captured from behind the subject  12 . The human body image  60  includes a body region (hereinafter referred to simply as a “body  62 ”). The body  62  also includes a right lung  64 , a left lung  66 , a heart  68 , a liver  70 , a stomach  72 , a spleen  74 , and a kidney  76  disposed inside of the body  62 . 
         [0044]    As shown in  FIG. 5B , the cross-sectional image group  36   b  makes up a three-dimensional image area  58   b . Similar to the case of  FIG. 5A , the image area  58   b  includes a human body image  60  with a body  62  having similar internal structural details. 
         [0045]    As shown in  FIG. 5C , the cross-sectional image group  36   c  makes up a three-dimensional image area  58   c . Similar to the case of  FIG. 5A , the image area  58   c  includes a human body image  60  with a body  62  having similar internal structural details. 
         [0046]    In a case where slice images of the subject  12  are captured along the body axis (the first through third image capturing processes) by the X-ray CT apparatus  24  (see  FIG. 1 ), a cross-sectional image group  36   a  ( 36   b ,  36   c ) is obtained, which represents a time series of cross-sectional images from an upper surface  78   a  ( 78   b ,  78   c ) to a lower surface  80   a  ( 80   b ,  80   c ). For example, the image capturing start position (the position of the upper surface  78   b ) in the second image capturing process differs from the image capturing start positions (positions of the upper surfaces  78   a ,  78   c ) in the first and third image capturing processes. Consequently, the human body image  60  in the image area  58   b  is positioned relatively higher than the human body images  60  in the image areas  58   a ,  58   c . The image processing conditions in the third image capturing process also differ from the image processing conditions in the first and second image capturing processes, in such a manner that the human body image  60  in the image area  58   c  is positioned relatively more to the left than the human body images  60  in the image areas  58   a ,  58   b.    
         [0047]    In step S 2 , the region recognizer  48  recognizes the regions of the body  62  that are represented by the respective cross-sectional image groups  36   a  through  36   c  acquired in step S 1 . Various known algorithms may be applied for recognizing regions of the body  62 . For example, among a plurality of reference regions, the region recognizer  48  may tentatively determine regions of the body  62  that are represented by the respective cross-sectional image groups  36   a  through  36   c , generate a cost map of costs established depending on combinations of the cross-sectional image groups  36   a  through  36   c  and the reference regions, and correct the regions in order to minimize the cost of the cost map (for details, see Japanese Patent No. 4855141). 
         [0048]    In step S 3 , the common region designator  50  designates a common region, which is commonly included in the cross-sectional image groups  36   a  through  36   c . According to the first embodiment, the common region designator  50  designates, as a common region, a region that identifies a position along the Z-axis (see  FIG. 4 ). The common region designator  50  may designate a common region according to an automatic instruction, based on the result of the image recognition process carried out in step S 2 , or according to an instruction entered by a doctor or a technician (hereinafter referred to as a “user”) through a non-illustrated input means. The common region designator  50  may detect a slice number in the cross-sectional image group  36   a , etc., from the Z-axis coordinate of the designated common region. 
         [0049]    The common region may represent an upper or lower end of an organ having a relatively large size, which may be a lung, a liver, or the like, or may represent an organ having a relatively small size, which may be a certain bone, a navel, or the like. The common region may be a region that can be identified uniquely from the shape of the organ, e.g., a certain branch of a bronchus, an upper edge of an aortic arc, a starting point or branch of a left or right coronary artery, a branch of a pulmonary artery or a vein, or a certain branch of a liver artery, a portal vein, or a vein. 
         [0050]    As shown in  FIG. 6A , it is assumed that the common region designator  50  has designated, as a common region  82 , the lower end (marked with a solid dot) of the left lung  66 , which is commonly included in the image areas  58   a  through  58   c . Among the image groups in the time-series cross-sectional image group  36 , one cross-sectional image group  36   a  is used as a positioning reference image (group), whereas the other cross-sectional image groups  36   b ,  36   c  are used as target images (groups). Reference numeral  84  represents a relative position of the common region  82  in the image area  58   a , which will hereinafter be referred to as a “target position  84 ”. 
         [0051]    In step S 4 , the positioning processor  52  positions the common region  82  along the Z-axis (axial direction) with respect to the time-series cross-sectional image group  36 . More specifically, the positioning processor  52  moves the image area  58   b  or the human body image  60  relatively based on the relationship between the target position  84  and the position (relative position) of the common region  82  in the image area  58   b.    
         [0052]    As shown in  FIG. 6B , the relative position (marked with a solid dot) of the common region  82  is located slightly higher than the target position  84  (marked with an outlined dot). The positioning processor  52  translates the image area  58   b  (or the human body image  60 ) upwardly (or downwardly) by a predetermined distance, thereby bringing the common region  82  into positional alignment with the target position  84 . At this time, a new cross-sectional image group (hereinafter referred to as an “adjusted image  90   b ”) is obtained, which represents a time series of cross-sectional images from an upper surface  86   b  to a lower surface  88   b.    
         [0053]    In step S 5 , the positioning processor  52  positions the common region  82  along an X-Y plane (cross-sectional direction) normal to the Z-axis (axial direction), with respect to the time-series cross-sectional image group  36 . More specifically, the positioning processor  52  moves the image area  58   c  or the human body image  60  relatively, based on the relationship between the target position  84  and the position (relative position) of the common region  82  in the image area  58   c.    
         [0054]    As shown in  FIG. 6C , the relative position (marked with a solid dot) of the common region  82  is located slightly more to the left than the target position  84  (marked with an outlined dot). The positioning processor  52  translates the image area  58   c  (or the human body image  60 ) leftward (or rightward) by a given distance, thereby bringing the common region  82  into positional alignment with the target position  84 . At this time, a new cross-sectional image group (hereinafter referred to as an “adjusted image  90   c ”) is obtained, which represents a time series of cross-sectional images from an upper surface  86   c  to a lower surface  88   c.    
         [0055]    Even in case images of the same region in the same subject  12  are captured, the shape and position of the region may become changed depending on the times at which the images are captured. More specifically, the shape and position of the region may be altered because the shape of the body of the subject  12  may be changed, and various environmental conditions, including body movements during times that the images are captured, may be changed. To cope with this problem, the positioning processor  52  may perform a registration process on voxel data. 
         [0056]    In step S 6 , the image compression processor  54  performs a moving-image compression process on the time-series images, and more specifically, the cross-sectional image group  36   a  as a positioning reference, and the adjusted images  90   b ,  90   c  that have been adjusted in steps S 4  and S 5 . The moving-image compression process may comprise any of various known digital moving-image compression processes including MPEG-2, MPEG-4 (Motion Picture Expanding Group), and may be a lossless compression process or a lossy compression process. In view of compression efficiency and processing time, any of various processes may be selected in relation to interframe prediction, motion compensation, transform coding, etc. 
         [0057]    As shown in  FIGS. 6A through 6C , the cross-sectional image group  36   a , the adjusted images  90   b ,  90   c , and the human body image  60  are generated three-dimensionally in substantially the same position. Image changes between such images are either zero or minimal. In other words, in case the cross-sectional image group  36   a  and the adjusted images  90   b ,  90   c  are arrayed in a time series of slices, and thereafter, the cross-sectional image group  36   a  and the adjusted images  90   b ,  90   c  are compressed, it is possible to obtain compressed data  42  having an extremely high compression ratio. This aspect of the present invention is highly effective, especially in case the structure of the imaged subject  12  is known, or in case the time-dependent change of the imaged subject  12  is comparatively small. 
         [0058]    In step S 7 , the image processing apparatus  26  stores the compressed data  42  that was obtained in step S 6  in the memory  32  or in an external device (storage device  28 ). At this time, the image processing apparatus  26  also stores the positioning data  40  that was generated in step S 5 , the positioning data being associated with the compressed data  42 . The image diagnosing apparatus  18  receives the compressed data  42  and the positioned data  40 , and thereafter, the image diagnosing apparatus  18  performs a predetermined expanding process and a positioning process (inverted transform) on the compressed data  42  and the positioned data  40  in order to recover the time-series cross-sectional image group  36 . 
       Second Embodiment 
       [0059]    A second embodiment for saving and managing a cross-sectional image group generated by capturing slice images will be described below with reference to  FIGS. 7 through 9 . 
         [0060]    In step S 11  of  FIG. 7 , the time-series image acquirer  46  acquires a cross-sectional image group  36   a  (see  FIGS. 4 and 7 ) as time-series images  44 . In step S 12 , the region recognizer  48  recognizes regions of a body, which are represented by the cross-sectional image group  36   a  that was acquired in step S 11 . Steps S 11  and S 12  are identical to steps S 1  and S 2  according to the first embodiment, and these steps will not be described in detail. 
         [0061]    In step S 13 , the common region designator  50  designates at least one common region, which is commonly included in the raw images Io(1) through Io(15) of the cross-sectional image group  36   a . According to the second embodiment, the common region designator  50  designates a region (structure) on which a positioning process is to be performed across a plurality of cross-sectional images. The common region designator  50  may designate, as a common region, at least one of a lung, a liver, a heart, a spleen, a kidney, a head, and a body. According to the second embodiment, it is assumed that the common region designator  50  designates a lung and a body. 
         [0062]    In step S 14 , the positioning processor  52  positions the common region along an X-Y plane (cross-sectional direction) with respect to the cross-sectional image group  36   a . More specifically, the positioning processor  52  positions the cross-sectional shape of the common region according to a rigid or a non-rigid registration process. 
         [0063]    As shown in  FIG. 8 , the raw image Io(1) of the cross-sectional image group  36   a  represents a body region (hereinafter referred to simply as a “body  100 ”) of the subject  12  (see  FIG. 1 ). The body  100 , which has a generally elliptical shape, includes a right lung region (hereinafter referred to simply as a “lung  102 ”), a left lung region (hereinafter referred to simply as a “lung  104 ”), and an esophagus region (hereinafter referred to simply as an “esophagus  106 ”). For illustrative purposes, only image backgrounds and region profiles are shown in  FIGS. 8 and 9 . 
         [0064]    The body  100  that is represented by the raw image Io(2) includes a right lung  108 , which differs in shape from the right lung  102 . More specifically, the right lung  108  bulges outwardly (to the left in  FIG. 8 ) compared to the right lung  102 . The differential region, which remains in a case where the right lung  102  is removed from the right lung  108 , is referred to as a bulging region  110 . The body  100  also includes a left lung  112 , which differs in shape from the left lung  104 . The left lung  112  bulges (to the right of  FIG. 8 ) compared to the left lung  104 . The differential region, which remains in a case where the left lung  104  is removed from the left lung  112 , is referred to as a bulging region  114 . 
         [0065]    The positioning processor  52  brings the common region that is represented by the raw image Io(2) into positional alignment with the common region that is represented by the raw image Io(1), which is adjacent to the raw image Io(2) in the times series. According to a non-rigid registration process, for example, the positioning processor  52  deforms the right lung  108  of the raw image Io(2) into a right lung  108   r , and deforms the left lung  112  of the raw image Io(2) into a left lung  112   r.    
         [0066]    Similar to the lungs, the body  100  also serves as a target to be positioned. However, the body  100  is of the same shape and remains in the same position in both of the raw images Io(1) and Io(2). Therefore, in spite of the positioning process, the body  100  remains unchanged in shape. 
         [0067]    The positioning processor  52  brings the common region that is represented by a raw image Io(n+1) (where n is an integer of 2 or greater), into positional alignment with the common region that is represented by a raw image Io(n) that is adjacent to the raw image Io(n+1) in the times series. Consequently, a new cross-sectional image group  120   a  is obtained, which represents a time series of cross-sectional images {Io(1), Ir(2; 1) through Ir(N; N−1)}. The adjusted image Ir(2; 1) represents a cross-sectional image generated by bringing the common region that is represented by the raw image Io(2) into positional alignment with the common region that is represented by the raw image Io(1). 
         [0068]    As shown in  FIG. 9 , the regions, which are represented by the raw image Io(1) and the adjusted image Ir(2; 1), are generated two-dimensionally in substantially the same position. Image changes between these images are either zero or minimal. In other words, in case the raw image Io(1) and the adjusted image Ir(2; 1) are arrayed in a time series of slices, and thereafter, the raw image Io(1) and the adjusted image Ir(2; 1) are compressed, it is possible to obtain compressed data  42  having an extremely high compression ratio. 
         [0069]    As described above, the image processing apparatus  26  includes the time-series image acquirer  46  that acquires time-series images  44  (the time-series cross-sectional image group  36 , the time-series cross-sectional images  38 ) that are generated in a time series, the common region designator  50  that designates at least one common region  82  ( 102 ,  104 ) that is commonly included in the acquired time-series images  44 , the positioning processor  52  that adjusts the time-series images  44  by bringing the common region  82  ( 108 ,  112 ) that is represented by at least one raw image of the time-series images  44  into positional alignment with the common region  82  ( 102 ,  104 ) that is represented by another raw image, and the image compression processor  54  that performs a moving-image compression process on new time-series images (the adjusted images  90   b ,  90   c , and the new cross-sectional image group  120   a ) which have been adjusted. 
         [0070]    Since the common region  82  ( 108 ,  112 ), which is represented by at least one raw image of the time-series images  44 , is brought into positional alignment with the common region  82  ( 102 ,  104 ), which is represented by another raw image, at least within the common region  82  ( 102 ,  104 ), it is possible to generate time-series images having a small interframe image change. Thus, in a case where the adjusted new time-series images are compressed, the time-series images can be compressed with increased efficiency. 
         [0071]    The present invention is not limited to the above embodiments, and various changes made be made freely to the embodiments without departing from the essence and scope of the invention. For example, the processing sequences according to the first and second embodiments may be combined with each other appropriately. In the above embodiments, specific examples of the compression process on medical images are illustrated. However, the present invention may also be applied to other fields apart from medical applications.