Patent Abstract:
Methods and apparatuses process images. The method according to one embodiment accesses digital image data representing an image including an object; accesses reference data including a shape model relating to shape variation of objects from a baseline object, the objects and the baseline object being from a class of the object; and removes from the image an element not related to the object, by representing a shape of the object using the shape model.

Full Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This non-provisional application is related to co-pending non-provisional applications titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique” and “Method and Apparatus of Using Probabilistic Atlas for Cancer Detection” filed concurrently herewith, the entire contents of which are hereby incorporated by reference. 
       BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The present invention relates to a digital image processing technique, and more particularly to a method and apparatus for processing breast images and using a shape model for feature removal/positioning in breast images. 
         [0004]    2. Description of the Related Art 
         [0005]    Mammography images are powerful tools used in diagnosis of medical problems of breasts. An important feature in mammography images is the breast shape. Clearly detected breast shapes can be used to identify breast abnormalities, such as skin retraction and skin thickening, which are characteristics of malignancy. Clear breast shapes also facilitate automatic or manual comparative analysis between mammography images. Accurate breast shapes may convey significant information relating to breast deformation, size, and shape evolution. The position of the nipple with respect to the breast can be used to detect breast abnormalities. Knowledge of the mammogram view is also important for analysis of breast images, since the mammogram view sets the direction and geometry of a breast in a mammogram image. 
         [0006]    Unclear or inaccurate breast shapes may obscure abnormal breast growth and deformation. Mammography images with unclear, unusual, or abnormal breast shapes or breast borders pose challenges when used in software applications that process and compare breast images. 
         [0007]    Due to the way the mammogram acquisition process works, the region where the breast tapers off has decreased breast contour contrast, which makes breast borders unclear and poses challenges for breast segmentation. Non-uniform background regions, tags, labels, or scratches present in mammography images may obscure the breast shape and create problems for processing of breast images. Reliable breast shape detection is further complicated by variations in anatomical shapes of breasts and medical imaging conditions. Such variations include: 1) anatomical shape variations between breasts of various people or between breasts of the same person; 2) lighting variations in breast images taken at different times; 3) pose and view changes in mammograms; 4) change in anatomical structure of breasts due to the aging of people; etc. Such breast imaging variations pose challenges for both manual identification and computer-aided analysis of breast shapes. 
         [0008]    Disclosed embodiments of this application address these and other issues by using methods and apparatuses for feature removal and positioning in breast images based on a shape modeling technique for breasts. The methods and apparatuses also use an atlas for location of features in breasts. The methods and apparatuses automatically determine views of mammograms using a shape modeling technique for breasts. The methods and apparatuses perform automatic breast segmentation, and automatically determine nipple position in breasts. The methods and apparatuses can be used for automatic detection of other features besides nipples in breasts. The methods and apparatuses can be used for feature removal, feature detection, feature positioning, and segmentation for other anatomical parts besides breasts, by using shape modeling techniques for the anatomical parts and atlases for locations of features in the anatomical parts. 
       SUMMARY OF THE INVENTION 
       [0009]    The present invention is directed to methods and apparatuses for processing images. According to a first aspect of the present invention, an image processing method comprises: accessing digital image data representing an image including an object; accessing reference data including a shape model relating to shape variation of objects from a baseline object, the objects and the baseline object being from a class of the object; and removing from the image an element not related to the object, by representing a shape of the object using the shape model. 
         [0010]    According to a second aspect of the present invention, an image processing method comprises: accessing digital image data representing an object; accessing reference data including a shape model relating to shape variation from a baseline object shape; and determining a view of the object, the determining step including performing shape registration for the object and for a mirror object of the object, by representing shapes of the object and of the mirror object using the shape model, to obtain an object registered shape and a mirror object registered shape, and identifying the view by performing a comparative analysis between at least one of the shape of the object, the shape of the mirror object, and the baseline object shape, and at least one of the object registered shape, the mirror object registered shape, and the baseline object shape. 
         [0011]    According to a third aspect of the present invention, an image processing method comprises: accessing digital image data representing an object; accessing reference data including a baseline object including an element, and a shape model relating to shape variation from the baseline object; and determining location of the element in the object, the determining step including generating a correspondence between a geometric part associated with the baseline object and a geometric part associated with the object, by representing a shape of the object using the shape model, to obtain a registered shape, and mapping the element from the baseline object onto the registered shape using the correspondence. 
         [0012]    According to a fourth aspect of the present invention, an image processing apparatus comprises: an image data input unit for providing digital image data representing an image including an object; a reference data unit for providing reference data including a shape model relating to shape variation of objects from a baseline object, the objects and the baseline object being from a class of the object; and a feature removal unit for removing from the image an element not related to the object, by representing a shape of the object using the shape model. 
         [0013]    According to a fifth aspect of the present invention, an image processing apparatus comprises: an image data input unit for providing digital image data representing an object; a reference data unit for providing reference data including a shape model relating to shape variation from a baseline object shape; and a view detection unit for determining a view of the object, the view detection unit determining a view by performing shape registration for the object and for a mirror object of the object, by representing shapes of the object and of the mirror object using the shape model, to obtain an object registered shape and a mirror object registered shape, and identifying the view by performing a comparative analysis between at least one of the shape of the object, the shape of the mirror object, and the baseline object shape, and at least one of the object registered shape, the mirror object registered shape, and the baseline object shape. 
         [0014]    According to a sixth aspect of the present invention, an image processing apparatus comprises: an image data input unit for providing digital image data representing an object; a reference data unit for providing reference data including a baseline object including an element, and a shape model relating to shape variation from the baseline object; and an element detection unit for determining location of the element in the object, the element detection unit determining location by generating a correspondence between a geometric part associated with the baseline object and a geometric part associated with the object, by representing a shape of the object using the shape model, to obtain a registered shape, and mapping the element from the baseline object onto the registered shape using the correspondence. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]    Further aspects and advantages of the present invention will become apparent upon reading the following detailed description in conjunction with the accompanying drawings, in which: 
           [0016]      FIG. 1  is a general block diagram of a system including an image processing unit for feature removal/positioning according to an embodiment of the present invention; 
           [0017]      FIG. 2  is a block diagram of an image processing unit for feature removal/positioning according to an embodiment of the present invention; 
           [0018]      FIG. 3  is a flow diagram illustrating operations performed by an image processing unit for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 2 ; 
           [0019]      FIG. 4  is a block diagram of an image processing unit for nipple detection according to an embodiment of the present invention illustrated in  FIG. 2 ; 
           [0020]      FIG. 5  is a flow diagram illustrating operations performed by an image operations unit included in an image processing unit for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 ; 
           [0021]      FIG. 6  is a flow diagram illustrating operations performed by a shape registration unit included in an image processing unit for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 ; 
           [0022]      FIG. 7  is a flow diagram illustrating exemplary operations performed by a feature removal and positioning unit included in an image processing unit for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 ; 
           [0023]      FIG. 8A  illustrates an exemplary baseline breast atlas shape with identified baseline nipple position for the ML view for a shape model stored in a reference data unit; 
           [0024]      FIG. 8B  illustrates exemplary deformation modes for a shape model stored in a reference data unit; 
           [0025]      FIG. 8C  illustrates another set of exemplary deformation modes for a shape model stored in a reference data unit; 
           [0026]      FIG. 8D  illustrates exemplary aspects of the operation of calculating a cost function by a shape registration unit for a registered shape according to an embodiment of the present invention illustrated in  FIG. 6 ; 
           [0027]      FIG. 8E  illustrates exemplary results of the operation of performing shape registration for breast masks by a shape registration unit according to an embodiment of the present invention illustrated in  FIG. 6 ; 
           [0028]      FIG. 8F  illustrates an exemplary ML view probabilistic atlas for probability of cancer in breasts stored in a reference data unit; 
           [0029]      FIG. 8G  illustrates an exemplary CC view probabilistic atlas for probability of cancer in breasts stored in a reference data unit; 
           [0030]      FIG. 8H  illustrates exemplary aspects of the operation of detecting nipple position for a breast image by an image processing unit for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 ; 
           [0031]      FIG. 8I  illustrates exemplary aspects of the operation of warping a breast mask to an atlas using triangulation by a feature removal and positioning unit according to an embodiment of the present invention illustrated in  FIG. 7 ; 
           [0032]      FIG. 8J  illustrates exemplary aspects of the operation of bilinear interpolation according to an embodiment of the present invention illustrated in  FIG. 7 ; 
           [0033]      FIG. 9  is a block diagram of an image processing unit for artifact removal and breast segmentation according to a second embodiment of the present invention illustrated in  FIG. 2 ; 
           [0034]      FIG. 10A  illustrates an exemplary output of an image processing unit for artifact removal and breast segmentation according to a second embodiment of the present invention illustrated in  FIG. 9 ; 
           [0035]      FIG. 10B  illustrates another exemplary output of an image processing unit for artifact removal and breast segmentation according to a second embodiment of the present invention illustrated in  FIG. 9 ; 
           [0036]      FIG. 11  is a block diagram of an image processing unit for view detection according to a third embodiment of the present invention illustrated in  FIG. 2 ; and 
           [0037]      FIG. 12  is a block diagram of an image processing unit for feature removal/positioning including a training system according to a fourth embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0038]    Aspects of the invention are more specifically set forth in the accompanying description with reference to the appended figures.  FIG. 1  is a general block diagram of a system including an image processing unit for feature removal/positioning according to an embodiment of the present invention. The system  100  illustrated in  FIG. 1  includes the following components: an image input unit  28 ; an image processing unit  38 ; a display  68 ; an image output unit  58 ; a user input unit  78 ; and a printing unit  48 . Operation of the system  100  in  FIG. 1  will become apparent from the following discussion. 
         [0039]    The image input unit  28  provides digital image data. Digital image data may be medical images such as mammogram images, brain scan images, X-ray images, etc. Digital image data may also be images of non-anatomical objects, images of people, etc. Image input unit  28  may be one or more of any number of devices providing digital image data derived from a radiological film, a diagnostic image, a photographic film, a digital system, etc. Such an input device may be, for example, a scanner for scanning images recorded on a film; a digital camera; a digital mammography machine; a recording medium such as a CD-R, a floppy disk, a USB drive, etc.; a database system which stores images; a network connection; an image processing system that outputs digital data, such as a computer application that processes images; etc. 
         [0040]    The image processing unit  38  receives digital image data from the image input unit  28  and performs feature removal/positioning in a manner discussed in detail below. A user, e.g., a radiology specialist at a medical facility, may view the output of image processing unit  38 , via display  68  and may input commands to the image processing unit  38  via the user input unit  78 . In the embodiment illustrated in  FIG. 1 , the user input unit  78  includes a keyboard  85  and a mouse  87 , but other conventional input devices could also be used. 
         [0041]    In addition to performing feature removal/positioning in accordance with embodiments of the present invention, the image processing unit  38  may perform additional image processing functions in accordance with commands received from the user input unit  78 . The printing unit  48  receives the output of the image processing unit  38  and generates a hard copy of the processed image data. In addition or as an alternative to generating a hard copy of the output of the image processing unit  38 , the processed image data may be returned as an image file, e.g., via a portable recording medium or via a network (not shown). The output of image processing unit  38  may also be sent to image output unit  58  that performs further operations on image data for various purposes. The image output unit  58  may be a module that performs further processing of the image data; a database that collects and compares images; a database that stores and uses feature removal/positioning results received from image processing unit  38 ; etc. 
         [0042]      FIG. 2  is a block diagram of an image processing unit  38  for feature removal/positioning according to an embodiment of the present invention. As shown in  FIG. 2 , the image processing unit  38  according to this embodiment includes: an image operations unit  128 ; a shape registration unit  138 ; a feature removal and positioning unit  148 ; and a reference data unit  158 . Although the various components of  FIG. 2  are illustrated as discrete elements, such an illustration is for ease of explanation and it should be recognized that certain operations of the various components may be performed by the same physical device, e.g., by one or more microprocessors. 
         [0043]    Generally, the arrangement of elements for the image processing unit  38  illustrated in  FIG. 2  performs preprocessing and preparation of digital image data, registration of shapes of objects from digital image data, and feature removal and positioning for objects in digital image data. Image operations unit  128  receives digital image data from image input unit  28 . Digital image data can be medical images, which may be obtained through medical imaging. Digital image data may be mammography images, brain scan images, chest X-ray images, etc. Digital image data may also be images of non-anatomical objects, images of people, etc. 
         [0044]    Operation of image processing unit  38  will be next described in the context of mammography images, for feature removal/positioning using a probabilistic atlas and/or a shape model for breasts. However, the principles of the current invention apply equally to other areas of image processing, for feature removal/positioning using a probabilistic atlas and/or a shape model for other types of objects besides breasts. 
         [0045]    Image operations unit  128  receives a set of breast images from image input unit  28  and may perform preprocessing and preparation operations on the breast images. Preprocessing and preparation operations performed by image operations unit  128  may include resizing, cropping, compression, color correction, etc., that change size and/or appearance of breast images. Image operations unit  128  may also extract breast shape information from breast images, and may store or extract information about breast images, such as views of mammograms. 
         [0046]    Image operations unit  128  sends the preprocessed breast images to shape registration unit  138 , which performs shape registration for breasts in the breast images. For shape registration, shape registration unit  138  represents breast shapes using a shape model, to obtain registered breast shapes. Shape registration unit  138  retrieves information about the shape model from reference data unit  158 , which stores parameters that define the shape model. Reference data unit  158  may also store one or more probabilistic atlases that include information about probability of breast structures at various locations inside breasts, and for various views of breasts recorded in mammograms. Breast structures recorded in probabilistic atlases may be, for example, cancer masses in breasts, benign formations in breasts, breast vessel areas, etc. 
         [0047]    Feature removal and positioning unit  148  receives registered breast shapes from shape registration unit  138 . Feature removal and positioning unit  148  retrieves data for a baseline breast image and/or data for a probabilistic atlas, from reference data unit  158 . Using retrieved data from reference data unit  158 , feature removal and positioning unit  148  performs removal of features and/or geometric positioning and processing for registered breast shapes. The output of feature removal and positioning unit  148  are breast images with identified features, and/or breast images from which certain features were removed. The output of feature removal and positioning unit  148  may also include information about locations of removed features or locations of other features of interest in breasts, information about orientation/view of breast images, etc. Feature removal and positioning unit  148  outputs breast images, together with positioning and/or feature removal information. Such output results may be output to image output unit  58 , printing unit  48 , and/or display  68 . 
         [0048]    Operation of the components included in image processing unit  38  illustrated in  FIG. 2  will be next described with reference to  FIG. 3 . Image operations unit  128 , shape registration unit  138 , feature removal and positioning unit  148 , and reference data unit  158  are software systems/applications. Image operations unit  128 , shape registration unit  138 , feature removal and positioning unit  148 , and reference data unit  158  may also be purpose built hardware such as FPGA, ASIC, etc. 
         [0049]      FIG. 3  is a flow diagram illustrating operations performed by an image processing unit  38  for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 2 . 
         [0050]    Image operations unit  128  receives a breast image from image input unit  28  (S 201 ). Image operations unit  128  performs preprocessing and preparation operations on the breast image (S 203 ). Preprocessing and preparation operations performed by image operations unit  128  may include resizing, cropping, compression, color correction, etc., that change size and/or appearance of breast images. Image operations unit  128  also extracts breast shape information from the breast image (S 205 ), and stores or extracts information about the view of the breast image (S 207 ). 
         [0051]    Image operations unit  128  sends the preprocessed breast image to shape registration unit  138 , which performs shape registration for the breast in the image to obtain a registered breast shape (S 209 ). For shape registration, shape registration unit  138  uses a shape model for breast shapes (S 211 ). The shape model describes how shape varies from breast to breast. The shape model is retrieved from reference data unit  158  (S 211 ). 
         [0052]    Feature removal and positioning unit  148  receives the registered breast shape from shape registration unit  138 . Feature removal and positioning unit  148  retrieves data describing a baseline breast image, which is included in the shape model, from reference data unit  158  (S 215 ). Feature removal and positioning unit  148  may also retrieve from reference data unit  158  data describing a probabilistic feature atlas (S 215 ). The probabilistic atlas includes information about probability of features at various locations inside breasts. Using the retrieved data from reference data unit  158 , feature removal and positioning unit  148  performs removal of features from the breast image and/or geometric positioning and processing for the registered breast shape (S 217 ). Feature removal and positioning unit  148  outputs the breast image with identified geometrical orientations, and/or from which certain features were removed (S 219 ). Such output results may be output to image output unit  58 , printing unit  48 , and/or display  68 . 
         [0053]      FIG. 4  is a block diagram of an image processing unit  38 A for nipple detection according to an embodiment of the present invention illustrated in  FIG. 2 . As shown in  FIG. 4 , the image processing unit  38 A according to this embodiment includes: an image operations unit  128 A; a shape registration unit  138 A; an atlas warping unit  340 ; a nipple detection unit  350 ; and a reference data unit  158 A. The atlas warping unit  340  and the nipple detection unit  350  are included in a feature removal and positioning unit  148 A. 
         [0054]    Image operations unit  128 A receives a set of breast images from image input unit  28 , and may perform preprocessing and preparation operations on the breast images. Preprocessing and preparation operations performed by image operations unit  128 A may include resizing, cropping, compression, color correction, etc., that change size and/or appearance of breast images. Image operations unit  128 A creates breast mask images including pixels that belong to the breasts in the breast images. Breast mask images are also called breast shape silhouettes in the current application. Breast mask images may be created, for example, by detecting breast borders or breast clusters, for the breasts shown in the breast images. Image operations unit  128 A may also store/extract information about breast images, such as views of the mammograms. 
         [0055]    Image operations unit  128 A sends the breast mask images to shape registration unit  138 A, which performs shape registration for breast mask images. For shape registration, shape registration unit  138 A describes breast mask images using a shape model, to obtain registered breast shapes. Shape registration unit  138 A retrieves information about the shape model from reference data unit  158 A, which stores parameters that define the shape model. 
         [0056]    Each mammogram view is associated with a shape model. A shape model may consist of a baseline breast atlas shape and a set of deformation modes. In one embodiment, the baseline breast atlas shape is a mean breast shape representing the average shape of a breast for a given mammogram view. Other baseline breast atlas shapes may also be used. The deformation modes define directions for deformation from contour points of breasts in the breast images, onto corresponding contour points of the breast in the baseline breast atlas shape. The shape model is obtained by training off-line, using large sets of training breast images. A baseline breast atlas shape can be obtained from the sets of training breast images. Deformation modes, describing variation of shapes of training breast images from the baseline breast atlas shape, are also obtained during training. Details on generation of a breast shape model using sets of training breast images can be found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0057]    A baseline breast atlas shape is generated during off-line training from a large number of training breast mask images. The baseline breast atlas shape may be, for example, a mean breast shape obtained by aligning centers of mass of training breast mask images. The alignment of centers of mass of training breast mask images results in a probabilistic map in which the brighter a pixel is, the more likely it is for the pixel to appear in a training breast mask image. A probability threshold may be applied to the probabilistic map, to obtain a mean breast shape in which every pixel has a high probability of appearing in a training breast mask image. Hence, the baseline breast atlas shape illustrates a baseline breast. Additional details regarding generation of a baseline breast atlas shape/mean breast shape can be found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. The baseline breast atlas shape also includes a baseline nipple for the baseline breast. The baseline nipple position is identified in the baseline breast atlas shape during off-line training. 
         [0058]    To extract deformation modes for a shape model, training breast mask images are warped onto the baseline breast atlas shape during off-line training, to define parameterization of breast shape. Control points may be placed along the edges of the baseline breast atlas shape. A deformation grid is generated using the control points. Using the deformation grid, the control points are warped onto training breast mask images. Shape representations for the training breast mask images are generated by the corresponding warped control points, together with centers of mass of the shapes defined by the warped control points. Additional details about generating shape representations for training breast images can be found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0059]    Principal modes of deformation between training breast mask images and the baseline breast atlas shape may be determined using the shape representations for the training breast mask images. Principal modes of deformation can be found using Principal Components Analysis (PCA) techniques. The principal components obtained from PCA represent modes of deformation between training breast mask images and the baseline breast atlas shape. Additional details regarding extraction of deformation modes are found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0060]    The baseline breast atlas shape, and the modes of deformation between training breast mask images and the baseline breast atlas shape define a shape model. Shape models can be obtained during off-line training, for each mammogram view. Shape models are stored in reference data unit  158 A. 
         [0061]    A new breast mask shape received from image operations unit  128 A may then be represented using a shape model from reference data unit  158 A. A breast mask shape may be expressed as a function of the baseline breast atlas shape, which may be a mean breast shape (B a ) in an exemplary embodiment, and of the shape model deformation modes, as: 
         [0000]    
       
         
           
             
               
                 
                   
                     Breast 
                      
                     
                         
                     
                      
                     Shape 
                   
                   = 
                   
                     p 
                     + 
                     
                       B 
                       a 
                     
                     + 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         k 
                       
                        
                       
                         
                           α 
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         [0000]    where p is an offset (such as a 2D offset) to the mean breast shape B a  to account for a rigid translation of the entire shape, L i , i=1 . . . k is the set of deformation modes of the shape model, and α i , i=1 . . . k are a set of parameters that define the deviations of Breast Shape from the mean breast shape along the axes associated with the principal deformation modes. The parameters α i , i=1 . . . k are specific to each breast mask. Hence, an arbitrary breast mask may be expressed as a sum of the fixed mean breast shape (B a ), a linear combination of fixed deformation modes L i  multiplied by coefficients α i , and a 2D offset p. Details on how a mean breast shape/baseline breast atlas shape B a  and deformation modes L i , i=1 . . . k are obtained during training, using training breast images can be found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0062]    Each mammogram view v i  is associated with a mean breast shape (B a     —     vi ) specific to that view, and with a set of deformation modes L i     —     vi , i=1 . . . k vi  specific to that view. 
         [0063]    For each breast mask image B mask     —     new  received from image operations unit  128 A, shape registration unit  138 A retrieves the mean breast shape (B a     —     vi ) and the set of deformation modes L i     —     vi , i=1 . . . k vi  associated with the view v i  of the breast mask image B mask     —     new  Shape registration unit  138 A next identifies the parameters α i , i=1 . . . k vi  and the 2D offset p for the breast mask image B mask     —     new , to fit the breast mask image B mask     —     new  with its correct shape representation of the form: 
         [0000]    
       
         
           
             
               Breast 
                
               
                   
               
                
               Shape 
             
             = 
             
               
                 B 
                 a_vi 
               
               + 
               p 
               + 
               
                 
                   ∑ 
                   
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                     = 
                     1 
                   
                   
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                     . 
                   
                 
               
             
           
         
       
     
         [0064]    Atlas warping unit  340  receives the registration results for the breast mask image B mask     —     new  from shape registration unit  138 A. Registration results for the breast mask image B mask     —     new  include the parameters α i , i=1 . . . k vi  for the breast mask image B mask     —     new  and the functional representation 
         [0000]    
       
         
           
             
               Breast 
                
               
                   
               
                
               Shape 
             
             = 
             
               
                 B 
                 a_vi 
               
               + 
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         [0000]    for the breast mask image B mask     —     new  Atlas warping unit  340  then warps the breast mask image B mask     —     new  to the mean breast shape B a     —     vi . Atlas warping unit  340  may, alternatively, warp the breast mask image B mask     —     new  to a probabilistic feature atlas A vi  specific to the view v i  of the breast mask image B mask     —     new . The probabilistic feature atlas data is stored in reference data unit  158 A. 
         [0065]    The probabilistic feature atlas A vi  includes an image of the mean breast shape B a     —     vi  for view v i , together with probabilities for presence of a feature at each pixel in the mean breast shape B a     —     vi . Hence, the probabilistic atlas A vi  is a weighted pixel image, in which each pixel of the mean breast shape B a     —     vi  is weighted by the feature probability for that pixel. 
         [0066]    The probabilistic feature atlas is obtained by training off-line, using large sets of training breast images with previously identified feature structures. Features recorded in probabilistic atlases may be cancer masses in breasts, benign formations in breasts, breast vessel areas, etc. The shapes of training breast images are represented as linear combinations of deformation modes obtained in training. Using the shape representations for the training breast images, previously identified features in the training breast images are mapped to the baseline breast atlas shape obtained in training. By overlapping feature positions from the training images onto the baseline breast atlas shape, a probabilistic atlas containing probabilities for presence of a feature in the baseline breast atlas shape is obtained. Additional details on generation of a probabilistic atlas using sets of training breast images with previously identified features can be found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0067]    After atlas warping unit  340  warps the breast mask image B mask     —     new  to the probabilistic atlas A vi  or to the mean breast shape B a     —     vi , a warped breast mask image B mask     —     new     —     warped  is obtained. Feature probability weights from the probabilistic atlas A vi  are associated with pixels in the warped image B mask     —     new     —     warped . The baseline nipple position from the mean breast shape B a     —     vi  is also associated with pixels in the warped image B mask     —     new     —     warped . 
         [0068]    Nipple detection unit  350  receives the warped breast mask image B mask     —     new  warped, together with shape registration information of the form Breast Shape= 
         [0000]    
       
         
           
             
               
                 B 
                 a_vi 
               
               + 
               p 
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                     i 
                     = 
                     1 
                   
                   
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                     α 
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                     i_vi 
                   
                 
               
             
             , 
           
         
       
     
         [0000]    that establishes a correspondence between pixels of B mask     —     new     —     warped  and pixels of B mask     —     new . 
         [0069]    Nipple detection unit  350  warps the B mask     —     new     —     warped  image back to the original B mask     —     new , and an image P mask     —     new , is obtained. Since the baseline nipple position has been identified in the baseline breast atlas shape during off-line training, and since B mask     —     new     —     warped  has the shape of the baseline breast atlas shape, the image P mask     —     new  includes a warped nipple position from B mask     —     new     —     warped  to B mask     —     new . Hence, the image P mask     —     new  is an image of the breast mask image B mask     —     new , in which the position of the nipple has been identified. Therefore, the image P mask     —     new  includes nipple detection results for the original breast mask B mask     —     new . 
         [0070]    If atlas warping unit  340  warped the breast mask image B mask     —     new  to probabilistic atlas A vi , the image P mask     —     new  includes feature probabilities for various breast features, at various pixel locations inside the breast mask image B mask     —     new . Hence, in this case, the image P mask     —     new  is a weighted pixel image, in which each pixel of the breast mask image B mask     —     new  is weighted by the feature probability for that pixel. If the feature is a cancer structure for example, the image P mask     —     new  is a weighted pixel image, in which each pixel of the breast mask image B mask     —     new  is weighted by the probability for cancer at that pixel. Additional details on mapping feature probabilities from a probabilistic atlas A vi  to a breast mask image B mask     —     new  to obtain a probability map for a feature in a breast mask image B mask     —     new  can be found in the co-pending non-provisional application titled “Method and Apparatus of Using Probabilistic Atlas for Cancer Detection”, the entire contents of which are hereby incorporated by reference. 
         [0071]    The identified nipple position in image P mask     —     new  provides very useful information for detection of the nipple and for the position of the nipple with respect to the breast. If the image P mask     —     new  includes cancer probabilities associated with pixels of the B mask     —     new  breast mask from a probabilistic cancer atlas, image P mask     —     new  provides information about the nipple position with respect to probable locations of cancer masses in the B mask     —     new  breast mask. The position of the nipple with respect to the breast can be used to detect breast abnormalities. Since the position of the nipple with respect to the breast is influenced by breast abnormalities, information about nipple position and nipple proximity to high probability cancer regions in breast help in identification of cancer masses, structural changes, breast abnormalities, etc. 
         [0072]    The initially identified nipple position in image P mask     —     new  (and hence in breast mask image B mask     —     new ) can also be a starting point for performing a refinement of the nipple position. Refinement of the nipple position can be performed, for example, in regions adjacent to or including the initially identified nipple position in image P mask     —     new . 
         [0073]    Nipple detection unit  350  outputs the image P mask     —     new . The image P mask     —     new  may be output to image output unit  58 , printing unit  48 , and/or display  68 . 
         [0074]    Image operations unit  128 A, shape registration unit  138 A, atlas warping unit  340 , nipple detection unit  350 , and reference data unit  158 A are software systems/applications. Image operations unit  128 A, shape registration unit  138 A, atlas warping unit  340 , nipple detection unit  350 , and reference data unit  158 A may also be purpose built hardware such as FPGA, ASIC, etc. 
         [0075]      FIG. 5  is a flow diagram illustrating operations performed by an image operations unit  128 A included in an image processing unit  38 A for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 . 
         [0076]    Image operations unit  128 A receives a raw or preprocessed breast image from image input unit  28  (S 401 ). The breast image may be retrieved by image operations unit  128 A from, for example, a breast imaging apparatus, a database of breast images, etc. Image operations unit  128 A may perform preprocessing operations on the breast image (S 403 ). Preprocessing operations may include resizing, cropping, compression, color correction, etc. 
         [0077]    Image operations unit  128 A creates a breast mask image for the breast image (S 405 ). The breast mask image includes pixels that belong to the breast. The breast mask image may be created by detecting breast borders for the breast shown in the breast image. Image operations unit  128 A may create a breast mask image by detecting breast borders using methods described in the U.S. patent application titled “Method and Apparatus for Breast Border Detection”, application Ser. No. 11/366,495, by Daniel Russakoff and Akira Hasegawa, filed on Mar. 3, 2006, the entire contents of which are hereby incorporated by reference. With the techniques described in the “Method and Apparatus for Breast Border Detection” application, pixels in the breast image are represented in a multi-dimensional space, such as a 4-dimensional space with x-locations of pixels, y-locations of pixels, intensity value of pixels, and distance of pixels to a reference point. K-means clustering of pixels is run in the multi-dimensional space, to obtain clusters for the breast image. Cluster merging and connected components analysis are then run using relative intensity measures, brightness pixel values, and cluster size, to identify a cluster corresponding to the breast in the breast image. A set of pixels, or a mask, containing breast pixels is obtained. The set of pixels for a breast forms a breast mask B mask . 
         [0078]    Other breast border detection techniques may also be used by image operations unit  128 A to obtain a breast mask image. 
         [0079]    Image operations unit  128 A also stores information about the breast image, such as information about the view of the mammogram (S 407 ). Examples of mammogram views are MLL (medio-lateral left), MLR (medio-lateral right), CCL (cranio-caudal left), CCR (cranio-caudal right), RCC, LRR, LMLO (left medio-lateral oblique), and RMLO (right medio-lateral oblique). Image operations unit  128 A outputs the breast mask image, and information about the view of the breast image (S 409 ), to shape registration unit  138 A. 
         [0080]      FIG. 6  is a flow diagram illustrating operations performed by a shape registration unit  138 A included in an image processing unit  38 A for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 . 
         [0081]    Shape registration unit  138 A receives from image operations unit  128 A a preprocessed breast image, represented as a breast mask image B mask     —     new  (S 470 ). Information about the mammogram view v i  of the breast image is also received (S 470 ). Shape registration unit  138 A retrieves from reference data unit  158 A data that defines the shape model for that view, including a mean breast shape (B a     —     vi ) and shape model deformation modes L i     —     vi , i=1 . . . k vi  for the view v i  of the breast mask image B mask     —     new  (S 472 ). 
         [0082]    Shape registration unit  138 A fits the breast mask image B mask     —     new  with its correct shape representation as a linear combination of the deformation modes, 
         [0000]    
       
         
           
             
               Shape 
               = 
               
                 
                   B 
                   a_vi 
                 
                 + 
                 p 
                 + 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     
                       k 
                       vi 
                     
                   
                    
                   
                     
                       α 
                       i 
                     
                      
                     
                       L 
                       i_vi 
                     
                   
                 
               
             
             , 
           
         
       
     
         [0000]    by determining parameters α i , i=1 . . . k vi  and the 2D offset p. 
         [0083]    To fit the breast mask image B mask     —     new  with its correct shape representation, shape registration unit  138 A optimizes the α i  values, together with an x offset p x  and a y offset p y , for a total of k+2 parameters: (p x , p y , α), where α=(α 1 , α 2 , . . . , α k ) and p=(p x , p y ) (S 478 ). For optimization, shape registration unit  138 A uses a cost function defined as the mean distance to edge. For a (p x , p y , α) parameter set, shape registration unit  138 A calculates the new shape resulting from this parameter set by formula 
         [0000]    
       
         
           
             Shape 
             = 
             
               
                 B 
                 a_vi 
               
               + 
               p 
               + 
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   
                     k 
                     vi 
                   
                 
                  
                 
                   
                     α 
                     i 
                   
                    
                   
                     
                       
                         L 
                         i_vi 
                       
                        
                       
                         ( 
                         
                           S 
                            
                           
                               
                           
                            
                           480 
                         
                         ) 
                       
                     
                     . 
                   
                 
               
             
           
         
       
     
         [0084]    The center of mass (Shape.COM) of Shape is then calculated (S 480 ). For each shape point on the exterior (border) of Shape, shape registration unit  138 A generates a ray containing the Shape.COM and the shape point, finds the intersection point of the ray with the edge of B mask     —     new , and calculates how far the shape point is from the intersection point obtained in this manner. This technique is further illustrated in  FIG. 8D . In an alternative embodiment, the minimum distance from the shape point to the edge of B mask     —     new  is calculated. The mean distance for the Shape points to the edges of the breast mask image B mask     —     new  is then calculated (S 482 ). Optimized α i  values and 2D offset p are selected for which the mean distance of shape points of Shape to the breast mask image B mask     —     new  edges attains a minimum (S 484 ). 
         [0085]    Shape registration unit  138 A may use the downhill simplex method, also known as the Nelder-Mead or the amoeba algorithm (S 486 ), to fit the breast mask image B mask     —     new  with its correct shape representation, by minimizing distances of the edge shape points of Shape to the edges of the breast mask image B mask     —     new . The downhill simplex method is a single-valued minimization algorithm that does not require derivatives. The downhill simplex algorithm is typically very robust. 
         [0086]    With the Nelder-Mead method, the k+2 parameters (p x , p y , α) form a simplex in a multi-dimensional space. The Nelder-Mead method minimizes the selected cost function, by moving points of the simplex to decrease the cost function. A point of the simplex may be moved by reflections against a plane generated by other simplex points, by reflection and expansion of the simplex obtained from a previous reflection, by contraction of the simplex, etc. 
         [0087]    Once parameters of the shape model are optimized for the breast mask image B mask     —     new , shape registration unit  138 A outputs the shape registration results for the breast mask image B mask     —     new  to atlas warping unit  301  (S 492 ). 
         [0088]      FIG. 7  is a flow diagram illustrating exemplary operations performed by a feature removal and positioning unit  148 A included in an image processing unit  38 A for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 .  FIG. 7  illustrates exemplary operations that may be performed by an atlas warping unit  340  and a nipple detection unit  350  included in a feature removal and positioning unit  148 A. 
         [0089]    Atlas warping unit  340  warps the registered shape for breast mask image B mask     —     new  to a probabilistic atlas A vi , or to a baseline breast atlas shape B a     —     vi , associated with the view v i  of the breast mask image B mask     —     new . Warping to probabilistic atlas A vi  or to the baseline breast atlas shape B a     —     vi  may be performed by triangulating the breast mask B mask     —     new  based on its center of mass and edge points (S 501 ). After shape registration has been performed by shape registration unit  138 A, each triangle in the breast mask B mask     —     new  corresponds to a triangle in the probabilistic atlas A vi  and to a triangle in the baseline breast atlas shape B a     —     vi  (S 503 ), as the probabilistic atlas A vi  has the shape of the baseline breast atlas shape B a     —     vi . Pixels inside corresponding triangles of the atlas A vi  (or B a     —     vi ) can be warped back and forth into triangles of breast mask B mask     —     new , using a bilinear interpolation in 2D (S 503 ). In an exemplary implementation, the bilinear interpolation in 2D may be performed by multiplying each of the triangle vertices by appropriate relative weights, as further described at  FIG. 8J . 
         [0090]    Nipple detection unit  350  warps back corresponding triangles of the atlas A vi  (or B a     —     vi ), to triangles in breast mask B mask     —     new  (S 505 ). The nipple position for the breast mask image B mask     —     new  is the warped nipple position from triangles of the baseline breast atlas shape B a     —     vi  (or probabilistic atlas A vi ) to triangles of the breast mask image B mask     —     new  (S 507 ). Hence, an image with a location for the nipple is obtained for the breast mask B mask     —     new  (S 507 ). 
         [0091]    Feature probabilities associated with pixels in triangles of the atlas image A vi  may become associated with pixels in triangles of breast mask B mask     —     new , as further described in the co-pending non-provisional application titled “Method and Apparatus of Using Probabilistic Atlas for Cancer Detection”, the entire contents of which are hereby incorporated by reference. Hence, the image with an identified nipple location may also contain feature probability values associated with image pixels, for features such as cancer structures, benign structures, etc. 
         [0092]      FIG. 8A  illustrates an exemplary baseline breast atlas shape for the ML view, with identified nipple position. The exemplary baseline breast atlas shape for the ML view is included in a shape model stored in a reference data unit  158 . The baseline breast atlas shape in  FIG. 8A  is a mean breast shape representing the set of pixels that have 95% or more chance of appearing in a breast mask image in the ML view. The nipple N has been identified on the mean breast shape. 
         [0093]      FIG. 8B  illustrates exemplary deformation modes for a shape model stored in the reference data unit  158 . The breast shape in figure I 510  is an exemplary baseline breast atlas shape (mean shape, in this case) for the ML view. 
         [0094]    The first 3 modes (L 1 , L 2 , L 3 ) of deformation are shown. The first mode of deformation is L 1 . Contours D 2  and D 3  define the deformation mode L 1 . The deformation mode L 1  consists of directions and proportional length of movement for each contour point from the D 2  contour to a corresponding contour point from the D 3  contour. Contours D 4  and D 5  define the second deformation mode L 2 , and contours D 6  and D 7  define the third deformation mode L 3 . 
         [0095]    The deformation modes shown in  FIG. 8B  may be obtained by training, using techniques described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0096]      FIG. 8C  illustrates another set of exemplary deformation modes for a shape model stored in the reference data unit  158 . The deformation modes shown in  FIG. 8C  were obtained by training a shape model using 4900 training breast images of ML view, using techniques described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 17 deformation modes, capturing 99% of the variance in the breast images data set, were obtained. The representations of the first 4 modes L 1 , L 2 , L 3  and L 4  are shown in  FIG. 8C . The representations of the first 4 modes L 1 , L 2 , L 3  and L 4  together capture 85% of the data&#39;s variance. For each mode shown in  FIG. 8C , the mean breast shape (baseline breast atlas shape) for the ML view is plotted with dots (points), while the arrows represent the distance traveled by one point for that mode from −2 standard deviations to +2 standard deviations of the mean breast shape. Mode L 1  captures 52% of the variance in the breast images data set, mode L 2  captures 18% of the variance in the breast images data set, mode L 3  captures 10% of the variance in the breast images data set, and mode L 4  captures 4% of the variance in the breast images data set. The rest of the deformation modes (L 5  to L 17 ) are not shown. 
         [0097]      FIG. 8D  illustrates exemplary aspects of the operation of calculating a cost function by a shape registration unit  138 A for a registered shape according to an embodiment of the present invention illustrated in  FIG. 6 . Shape registration is performed for the breast mask B mask     —     new  I 511  using an α i , i=1 . . . k parameter set and a 2D offset p. A shape bounded by contour C 512  is obtained from formula 
         [0000]    
       
         
           
             
               Shape 
               = 
               
                 
                   B 
                   a_vi 
                 
                 + 
                 p 
                 + 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     k 
                   
                    
                   
                     
                       α 
                       i 
                     
                      
                     
                       L 
                       i_vi 
                     
                   
                 
               
             
             , 
           
         
       
     
         [0000]    where B a     —     vi  is a mean breast shape for view v i  of the breast mask B mask     —     new , and L i     —     vi , i=1 . . . k vi  are shape model deformation modes. The center of mass COM for the Shape bounded by contour C 512  is found. For a point S 1  on the contour (perimeter) of Shape, a line is drawn through the COM point. The line intersects the contour of breast mask B mask     —     new  I 511  at point S 2 . The distance to edge is the distance d between points S 1  and S 2 . Distances d are obtained for all points on the contour (perimeter) C 512  of Shape, and a cost function is obtained as the mean of all distances d. 
         [0098]      FIG. 8E  illustrates exemplary results of the operation of performing shape registration for breast masks by a shape registration unit  138 A according to an embodiment of the present invention illustrated in  FIG. 6 . As shown in  FIG. 8E , breast masks I 513  and I 514  are fit with shape representations. The shape registration results bounded by contours C 513  and C 514  are effectively describing the shapes of breast masks I 513  and I 514 . The downhill simplex algorithm was used by shape registration unit  138 A to obtain the shape registration results shown in  FIG. 8E . 
         [0099]      FIG. 8F  illustrates an exemplary ML view probabilistic atlas for probability of cancer in breasts stored in the reference data unit  158 . For the ML view probabilistic atlas in  FIG. 8F , the contour C 515  is the contour of the mean breast shape (baseline breast atlas shape) B a     —     ML  for the ML view. The region R 515 A indicates the highest probability of cancer, followed by regions R 515 B, then R 515 C, and R 515 D. As shown in the probabilistic atlas, the probability for cancer is largest in the center of a breast, and decreases towards edges of the mean breast shape. 
         [0100]      FIG. 8G  illustrates an exemplary CC view probabilistic atlas for probability of cancer in breasts stored in the probabilistic atlas reference data unit  158 . For the CC view probabilistic atlas in  FIG. 8G , the contour C 516  is the contour of the mean breast shape for the CC view. The region R 516 A indicates the highest probability of cancer, followed by regions R 516 B, then R 516 C, and R 516 D. As shown in the probabilistic atlas, the probability for cancer is largest in the center left region of a breast, and decreases towards edges of the mean breast shape. 
         [0101]      FIG. 8H  illustrates exemplary aspects of the operation of detecting nipple position for a breast image by an image processing unit  38 A for feature removal/positioning according to an embodiment of the present invention illustrated in  FIG. 4 . As illustrated in  FIG. 8H , a breast image I 518  is input by image operations unit  128 A. Image operations unit  128 A extracts a breast mask image I 519  for the breast image I 518 . Shape registration unit  138 A performs shape registration for the breast mask image, by representing the shape of the breast mask using a shape model. The shape registration contour C 520  fits the shape of the breast mask from image I 519 . Atlas warping unit  340  warps the breast mask registered shape I 520  to a probabilistic atlas (or alternatively to a baseline breast atlas shape) I 522  that includes a detected baseline nipple N. Atlas warping unit  340  performs warping by generating a correspondence between pixels of the breast mask registered shape I 520  and pixels of the probabilistic atlas (or of the baseline breast atlas shape) I 522 . Using the correspondence, nipple detection unit  350  warps the probabilistic atlas (or baseline breast atlas shape) I 522  onto the breast mask registered shape I 520 , hence obtaining an image I 523  with detected nipple position N′ corresponding to the baseline nipple position N, for the breast image I 518 . 
         [0102]      FIG. 8I  illustrates exemplary aspects of the operation of warping a breast mask to an atlas using triangulation by a feature removal and positioning unit  148 A according to an embodiment of the present invention illustrated in  FIG. 7 . 
         [0103]    Atlas warping unit  340  warps a registered shape S 530  for a breast mask image B mask     —     new  I 530  to a probabilistic atlas A vi  (or to a baseline breast atlas shape) A 532  shown in image I 532 . Warping to probabilistic atlas A vi  (or to baseline breast atlas shape) A 532  is performed by triangulating the breast mask shape S 530  based on its center of mass COM_ 530  and edge points. A test point P_ 530  is used to generate a triangle in the breast mask shape S 530 . For example, a triangle T_ 530  is generated using the center of mass COM_ 530  and the test point P_ 530  and touching the edges of mask shape S 530 . The triangle is warped to probabilistic atlas A vi  (or to baseline breast atlas shape) A 532  onto a corresponding triangle T_ 532 , with the COM_ 530  and the test point P_ 530  mapped to corresponding points PC_ 532  and P_ 532 . The probabilistic atlas A vi  (or baseline breast atlas shape) A 532  is then warped onto registered shape S 530  by warping each triangle T_ 532  back onto the corresponding triangle T_ 530  of the breast mask B mask     —     new  I 530 . The nipple position the probabilistic atlas A vi  (or baseline breast atlas shape) A 532  is hence warped onto registered shape S 530  associated with the breast mask image B mask     —     new  I 530 . 
         [0104]      FIG. 8J  illustrates exemplary aspects of the operation of bilinear interpolation according to an embodiment of the present invention illustrated in  FIG. 7 . The pixels inside corresponding triangles of the atlas A vi  (or baseline breast atlas shape B a     —     vi ) can be warped back and forth to triangles in breast mask B mask     —     new , using a bilinear interpolation. For a correspondence between two triangles, bilinear interpolation in 2D is performed by multiplying each of the vertices by appropriate relative weights as described in  FIG. 8J . Given a triangle with vertices A, B, and C, the pixel intensity at point D can be obtained as: 
         [0000]        D=A*wA/T   abc   +B*wB/T   abc   +C*wC/T   abc   (2) 
         [0000]    where A, B, and C are pixel intensities at triangle vertices, T abc  is the area of triangle ABC, wA is the area of triangle BCD, wB is the area of triangle ACD, and wC is the area of triangle ABD, so that T abc =wA+wB+wC. Hence, given pixels A, B, and C of a triangle inside atlas A vi  (or inside B a     —     vi ), and corresponding pixels A′, B′, and C′ of a corresponding triangle in breast mask B mask     —     new , a pixel D inside triangle ABC can be warped to a pixel D′ inside triangle A′B′C′, using equation (2) in triangle A′B′C′. 
         [0105]      FIG. 9  is a block diagram of an image processing unit  38 B for artifact removal and breast segmentation according to a second embodiment of the present invention illustrated in  FIG. 2 . As shown in  FIG. 9 , the image processing unit  38 B according to this embodiment includes: an image operations unit  128 B; a shape registration unit  138 B; an optional atlas warping unit  340 ; an artifact removal unit  360 ; and a reference data unit  158 B. The atlas warping unit  340  and the artifact removal unit  360  are included in a feature removal and positioning unit  148 B. 
         [0106]    Image operations unit  128 B receives a breast image from image input unit  28 , and may perform preprocessing and preparation operations on the breast image. Preprocessing and preparation operations performed by image operations unit  128 B may include resizing, cropping, compression, color correction, etc., that change size and/or appearance of the breast image. Image operations unit  128 B creates a breast mask image. Breast mask images may be created, for example, by detecting breast borders or breast clusters for the breasts shown in the breast image. Image operations unit  128 B may also store/extract information about the breast image, such as view of mammogram. 
         [0107]    Image operations unit  128 B may perform preprocessing and breast mask extraction operations in a similar manner to image operations unit  128 A described in  FIG. 5 . Image operations unit  128 B may create a breast mask image by detecting breast borders using methods described in the U.S. patent application titled “Method and Apparatus for Breast Border Detection”, application Ser. No. 11/366,495, by Daniel Russakoff and Akira Hasegawa, filed on Mar. 3, 2006, the entire contents of which are hereby incorporated by reference. Other methods may also be used to create a breast mask image. 
         [0108]    Image operations unit  128 B sends the breast mask images to shape registration unit  138 B, which performs shape registration for the breast mask image. For shape registration, shape registration unit  138 B describes the breast mask image using a shape model, to obtain a registered breast shape. Shape registration unit  138 B retrieves information about the shape model from reference data unit  158 B, which stores parameters that define the shape model. 
         [0109]    The reference data unit  158 B is similar to reference data unit  158 A from  FIG. 4 . Reference data unit  158 B stores shape models, and may also store probabilistic atlases for breast features. A shape model and an optional probabilistic atlas stored by reference data unit  158 B can be generated off-line, using training breast images. Details on generation of a breast shape model and a probabilistic atlas using sets of training breast images can be found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. A shape model stored by reference data unit  158 B includes a baseline breast atlas image and a set of deformation modes. A shape model stored by reference data unit  158 B is similar to a shape model stored by reference data unit  158 A as described at  FIG. 4 , with two differences. One difference is that the nipple of the baseline breast atlas shape need not be identified and marked for the baseline breast atlas shape stored by reference data unit  158 B. The second difference pertains to the method of generation of the shape model during off-line training. The training breast images used to generate the shape model for reference data unit  158 B off-line are preferably breast images without artifacts (such as tags, noise, frames, image scratches, lead markers, imaging plates, etc.), anomalies, or unusual structures. Training breast images without artifacts may be obtained by removing artifacts, anomalies, or unusual structures from the images manually or automatically, before off-line training. In that case, the baseline breast atlas shape obtained as described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference, illustrates a baseline breast without artifacts, anomalies, or unusual structures. The deformation modes obtained as described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference, describe variations between shapes of training breast images and the baseline breast atlas shape. Hence, linear combinations of the deformation modes will produce breast shapes without artifacts, anomalies, or unusual structures, because the deformation modes were obtained from training breast images that did not include artifacts, anomalies, or unusual structures. Reference data unit  158 B stores information for shape models for breasts, for various views of mammograms. 
         [0110]    Shape registration unit  138 B may perform shape registration in a manner similar to shape registration unit  138 A, as described at  FIG. 6 . Optional atlas warping unit  340  receives the registration results for a breast mask image from shape registration unit  138 B, and warps the breast mask image to the baseline breast atlas shape from the shape model associated with the view of the breast mask image. Atlas warping unit  340  performs warping of breast mask images to baseline breast atlas shapes or to probabilistic atlases, as described in  FIG. 4  and  FIG. 7 . 
         [0111]    Using the image processing unit  38 B it is possible to remove artifacts, such as tags, noise, frames, image scratches, lead markers, imaging plates, etc., from a breast image and perform an accurate segmentation of the breast in the breast image. For a breast image I T  including artifacts, image operations unit  128 B obtains a breast mask image B T     —     mask . Shape registration unit  138 B then performs shape registration for the breast mask image B T     —     mask . Shape registration unit  138 B expresses the breast mask image B T     —     mask  as a function of the baseline breast atlas shape, which may be a mean breast shape (B a ), and shape model deformation modes, as: 
         [0000]    
       
         
           
             
               
                 Breast 
                  
                 
                     
                 
                  
                 Shape 
               
               = 
               
                 B 
                 + 
                 p 
                 + 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     k 
                   
                    
                   
                     
                       α 
                       i 
                     
                      
                     L 
                   
                 
               
             
             , 
           
         
       
     
         [0000]    where L i , i=1 . . . k is the set of deformation modes of the shape model, α 1 , i=1 . . . k are a set of parameters optimized by shape registration unit  138 B for breast mask image B T     —     mask , and p is an offset (such as a 2D offset) to the mean breast shape B a  to account for a rigid translation of the entire shape. Shape registration unit  138 B retrieves baseline breast atlas shape data and deformation modes from reference data unit  158 B. Since the shape model stored in reference data unit  158 B was generated using training breast shape images without artifacts, anomalies, or unusual structures, the Breast Shape obtained from 
         [0000]    
       
         
           
             
               Breast 
                
               
                   
               
                
               Shape 
             
             = 
             
               B 
               + 
               p 
               + 
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   k 
                 
                  
                 
                   
                     α 
                     i 
                   
                    
                   L 
                 
               
             
           
         
       
     
         [0000]    with optimized α i  and p parameters will not include artifacts, anomalies, or unusual structures. In other words, the Breast Shape will optimize a fit to the original breast mask image B T     —     mask , except for the artifacts that were present in the original breast mask image B T     —     mask . The artifacts present in the original breast mask image B T     —     mask  have not been learned by the shape model stored in reference data unit  158 B, and will not be fit. Hence, the Breast Shape represents a segmentation of the breast in the breast mask image B T     —     mask , without the artifacts may have been present in breast mask image B T     —     mask . 
         [0112]    Artifact removal unit  360  receives the Breast Shape together with the breast mask image B T     —     mask  from shape registration unit  138 B, and may extract artifacts by subtracting the Breast Shape from the breast mask image B T     —     mask , to obtain an artifact mask image I Art    
         [0113]    Artifact removal unit  360  can then apply the artifact mask image I Art  to the original breast image I T , to identify artifact positions in the original breast image I T  and remove the artifacts. Artifact removal unit  360  outputs a breast image I T ′ without artifacts. 
         [0114]    If the reference data unit  158 B contains a probabilistic feature atlas, and atlas warping unit  340  is present in image processing unit  38 B, breast segmentation with artifact removal may be combined with feature detection. For example, artifact removal may be achieved for an original breast image I T  together with cancer detection using a probabilistic cancer atlas and/or comparative left-right breast analysis, as described in the co-pending non-provisional application titled “Method and Apparatus of Using Probabilistic Atlas for Cancer Detection”, the entire contents of which are hereby incorporated by reference. 
         [0115]    Image operations unit  128 B, shape registration unit  138 B, optional atlas warping unit  340 , artifact removal unit  360 , and reference data unit  158 B are software systems/applications. Image operations unit  128 B, shape registration unit  138 B, optional atlas warping unit  340 , artifact removal unit  360 , and reference data unit  158 B may also be purpose built hardware such as FPGA, ASIC, etc. 
         [0116]      FIG. 10A  illustrates an exemplary output of an image processing unit  38 B for artifact removal and breast segmentation according to a second embodiment of the present invention illustrated in  FIG. 9 . A breast mask I 581  with a tag T 582  is segmented by image processing unit  35 B using a shape model that was constrained to remain within the shape space of typical breasts without artifacts. The final segmented breast shape I 583  obtained by image processing unit  35 B does not contain the tag T 582 , as the segmented breast shape is constrained by the shape model to resemble a breast. 
         [0117]      FIG. 10B  illustrates another exemplary output of an image processing unit  38 B for artifact removal and breast segmentation according to a second embodiment of the present invention illustrated in  FIG. 9 . A breast mask I 591  with a skin fold T 592  is segmented by image processing unit  35 B using a shape model that was constrained to remain within the shape space of typical breasts without artifacts. The final segmented breast shape I 593  obtained by image processing unit  35 B does not contain the skin fold T 592 , as the segmented breast shape is constrained by the shape model to resemble a breast. 
         [0118]      FIG. 11  is a block diagram of an image processing unit  38 C for view detection according to a third embodiment of the present invention illustrated in  FIG. 2 . As shown in  FIG. 11 , the image processing unit  38 C according to this embodiment includes: an image operations unit  128 C; a shape registration unit  138 C; a view decision unit  148 C; and a reference data unit  158 C. The view decision unit  148 C is a feature removal and positioning unit. 
         [0119]    Image operations unit  128 C receives a breast image from image input unit  28 , and may perform preprocessing and preparation operations on the breast image. Preprocessing and preparation operations performed by image operations unit  128 C may include resizing, cropping, compression, color correction, etc., that change size and/or appearance of the breast image. Image operations unit  128 C creates a breast mask image. Breast mask images may be created, for example, by detecting breast borders or breast clusters for the breasts shown in the breast image. Image operations unit  128 C may also store/extract information about the breast image, such as view of mammogram. 
         [0120]    Image operations unit  128 C may perform preprocessing and breast mask extraction operations in a similar manner to image operations unit  128 A described in  FIG. 5 . Image operations unit  128 C may create a breast mask image by detecting breast borders using methods described in the U.S. patent application titled “Method and Apparatus for Breast Border Detection”, application Ser. No. 11/366,495, by Daniel Russakoff and Akira Hasegawa, filed on Mar. 3, 2006, the entire contents of which are hereby incorporated by reference. 
         [0121]    Image operations unit  128 C sends the breast mask images to shape registration unit  138 C, which performs shape registration for the breast mask image. For shape registration, shape registration unit  138 C describes the breast mask image using a shape model, to obtain a registered breast shape. Shape registration unit  138 C retrieves information about the shape model from reference data unit  158 C, which stores parameters that define the shape model. 
         [0122]    The reference data unit  158 C is similar to reference data unit  158 A from  FIG. 4 . The reference data unit  158 C stores shape models, and may also store probabilistic atlases. 
         [0123]    A shape model stored by reference data unit  158 C can be generated off-line, using training breast images. Details on generation of a breast shape model using sets of training breast images can be found in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. A shape model stored by reference data unit  158 C includes a baseline breast atlas image and a set of deformation modes. 
         [0124]    Shape registration unit  138 C may perform shape registration in a manner similar to shape registration unit  138 A, as described at  FIG. 6 . Shape registration unit  138 C receives from image operations unit  128 C a breast mask image B mask  of unknown mammogram view. B mask  could be, for example, an ML mammogram view for which the view direction of left or right is not known. 
         [0125]    Shape registration unit  138 C fits the breast mask image B mask  to a shape model M associated with one of left or right views, and obtains a registered image R 1 . Shape registration unit  138 C then flips the breast mask image B mask  about a vertical axis to obtain a flipped breast mask B mask     —     Flipped , and then fits the flipped breast mask image B mask     —     Flipped  to the same shape model M, to obtain a registered image R 2 . 
         [0126]    View detection unit  148 C receives breast mask images B mask  and B mask     —     Flipped , and registered images R 1  and R 2 . View detection unit  148 C then compares the fit of R 1  to B mask , and the fit of R 2  to B mask     —     Flipped . If the fit of R 1  to B mask  is better than the fit of R 2  to B mask     —     Flipped , then the view associated with shape model M is the view of the breast image B mask . On the other hand, if the fit of R 2  to B mask     —     Flipped  is better than the fit of R 1  to B mask , then the view associated with shape model M is the view of breast image B mask     —     Flipped . The view direction of the breast mask image B mask  is hence detected. View detection results are output to printing unit  48 , display  68 , and/or image output unit  58 . 
         [0127]    The view of breast mask image B mask  may also be detected by comparison to a baseline shape. Let B a  be the baseline breast atlas shape associated with the shape model M. View detection unit  148 C compares the differences between R 1  and B a , and the differences between R 2  and B a . If the differences between R 1  and B a  are smaller than the differences between R 2  and B a , then the view associated with baseline breast atlas shape B a  (and hence with shape model M) is the view of breast image B mask . On the other hand, if the differences between R 2  and B a  are smaller than the differences between R 1  and B a , then the view associated with baseline breast atlas shape B a  (and hence with shape model M) is the view of breast image B mask     —     Flipped . 
         [0128]    The view of breast mask images B mask  may also be detected by direct comparison of B mask  and B mask     —     Flipped  with B a , without performing shape registration of B mask  and B mask     —     Flipped . If the differences between B mask  and B a  are smaller than the differences between B mask     —     Flipped  and B a , then the view associated with baseline breast atlas shape B a  is the view of breast image B mask . On the other hand, if the differences between B mask  and B a  are larger than the differences between B mask     —     Flipped  and B a , then the view associated with baseline breast atlas shape B a  is the view of breast image B mask     —     Flipped . 
         [0129]      FIG. 12  is a block diagram of an image processing unit  39  for feature removal/positioning including a training system  772  according to a fourth embodiment of the present invention. As shown in  FIG. 12 , the image processing unit  39  includes the following components: an image operations unit  620 ; a baseline shape unit  710 ; a shape parameterization unit  720 ; a deformation analysis unit  730 ; a training shape registration unit  740 ; an atlas output unit  750 ; an image operations unit  128 ; a shape registration unit  138 ; a feature removal and positioning unit  148 ; and a reference data unit  158 . Image operations unit  620 , baseline shape unit  710 , shape parameterization unit  720 , deformation analysis unit  730 , training shape registration unit  740 , and atlas output unit  750  are included in a training system  772 . Training shape registration unit  740  and atlas output unit  750  are optional, and may be included depending on the application. Image operations unit  128 , shape registration unit  138 , feature removal and positioning unit  148 , and reference data unit  158  are included in an operation system  38 . 
         [0130]    Operation of the image processing unit  39  can generally be divided into two stages: (1) training; and (2) operation for positioning and for feature removal or detection. 
         [0131]    The principles involved in the training stage have been described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. In accordance with this fourth embodiment illustrated in  FIG. 12 , the image operations unit  620 , baseline shape unit  710 , shape parameterization unit  720 , deformation analysis unit  730 , training shape registration unit  740 , and atlas output unit  750  train to generate a shape model and a probabilistic feature atlas for breast shapes. The knowledge accumulated through training by training system  772  is sent to reference data unit  158 . Image operations unit  620  and shape model unit  630  trains to generate a shape model. Optional probabilistic atlas generation unit  640  trains to generate a probabilistic atlas. The shape model and the probabilistic atlas are sent and stored in reference data unit  158 . 
         [0132]    In accordance with this fourth embodiment of the present invention, the image operations unit  128 , the shape registration unit  138 , the feature removal and positioning unit  148 , and the reference data unit  158  may function in like manner to the corresponding elements of the first, second, or third embodiments illustrated in  FIGS. 4 ,  9 , and  11 , or as a combination of two or more of the first, second, and third embodiments illustrated in  FIGS. 4 ,  9 , and  11 . During regular operation of image processing unit  39 , reference data unit  158  provides reference data training knowledge to shape registration unit  138  and to feature removal and positioning unit  148 , for use in nipple detection, view detection, and artifact removal from breast images. The principles involved in the operation for nipple detection for new breast images have been described in  FIGS. 4 ,  5 ,  6 ,  7 ,  8 A,  8 B,  8 C,  8 D,  8 E,  8 F,  8 G,  8 H,  8 I and  8 J. The principles involved in the operation for artifact removal from breast images have been described in  FIGS. 9 ,  5 ,  6 ,  7 ,  8 A,  8 B,  8 C,  8 D,  8 E,  8 F,  8 G,  8 H,  8 I and  8 J. The principles involved in the operation for view detection for new breast images have been described in  FIGS. 11 ,  5 ,  6 ,  7 ,  8 A,  8 B,  8 C,  8 D,  8 E,  8 F,  8 G,  8 H,  8 I and  8 J. 
         [0133]    During the training stage, image operations unit  620  receives a set of training breast images from image input unit  28 , performs preprocessing and preparation operations on the breast images, creates training breast mask images, and stores/extracts information about breast images, such as view of mammograms. Additional details regarding operation of image operations unit  620  are described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. Image operations unit  620  may create breast mask images by extracting breast borders using methods described in the U.S. patent application titled “Method and Apparatus for Breast Border Detection”, application Ser. No. 11/366,495, by Daniel Russakoff and Akira Hasegawa, filed on Mar. 3, 2006, the entire contents of which are hereby incorporated by reference. Other breast border detection techniques can also be used by image operations unit  620  to obtain shape mask images for breast images. 
         [0134]    Baseline shape unit  710  receives training breast mask images from image operations unit  620 , and generates a baseline breast atlas shape such as, for example, a mean breast shape, from the training breast mask images. Baseline shape unit  710  may align the centers of mass of the training breast mask images. The alignment of centers of mass of training breast mask images results in a probabilistic map in which the brighter a pixel is, the more likely it is for the pixel to appear in a training breast mask image. A probability threshold may then be applied to the probabilistic map, to obtain a baseline breast atlas shape, such as, for example, a mean breast shape. Additional details regarding operation of baseline shape unit  710  are described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0135]    Shape parameterization unit  720  receives the training breast mask images and the baseline breast atlas shape, and warps the training breast mask images onto the baseline breast atlas shape, to define parameterization of breast shape. Shape parameterization unit  720  may use shape parameterization techniques adapted from “Automatic Generation of Shape Models Using Nonrigid Registration with a Single Segmented Template Mesh” by G. Heitz, T. Rohlfing and C. Maurer, Proceedings of Vision, Modeling and Visualization, 2004, the entire contents of which are hereby incorporated by reference. Control points may be placed along the edges of the baseline breast atlas shape. A deformation grid is generated using the control points. Using the deformation grid, the control points are warped onto training breast mask images. Shape information for training breast mask images is then given by the corresponding warped control points together with centers of mass of the shapes defined by the warped control points. Warping of control points from the baseline breast atlas shape onto training breast mask images may be performed by non-rigid registration, with B-splines transformations used to define warps from baseline breast atlas shape to training breast mask images. Shape parameterization unit  720  may perform non-rigid registration using techniques discussed in “Automatic Construction of 3-D Statistical Deformation Models of the Brain Using Nonrigid Registration”, by D. Rueckert, A. Frangi and J. Schnabel, IEEE Transactions on Medical Imaging, 22(8), p. 1014-1025, August 2003, the entire contents of which are hereby incorporated by reference. Shape parameterization unit  720  outputs shape representations for training breast mask images. Additional details regarding operation of shape parameterization unit  720  are described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0136]    Deformation analysis unit  730  uses breast shape parameterization results to learn a shape model that describes how shape varies from breast to breast. Using representations of shape for the training breast mask images, deformation analysis unit  730  finds the principal modes of deformation between the training breast mask images and the baseline breast atlas shape. Deformation analysis unit  730  may use Principal Components Analysis (PCA) techniques to find the principal modes of deformation. The principal components obtained from PCA represent modes of deformation between training breast mask images and the baseline breast atlas shape. Additional details regarding operation of deformation analysis unit  730  are described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0137]    The baseline breast atlas shape and the modes of deformation between training breast mask images and the baseline breast atlas shape, define a shape model. A shape model can be obtained for each mammogram view. Shape model information is sent to reference data unit  158 , to be used during operation of image processing unit  39 . 
         [0138]    Training shape registration unit  740  receives data that defines the shape model. Training shape registration unit  740  then fits training breast mask images with their correct shape representations, which are linear combinations of the principal modes of shape variation. Shape registration unit  740  may use the downhill simplex method, also known as the Nelder-Mead or the amoeba algorithm, to optimize parameters of the shape model for each training breast mask image in the training dataset, and optimally describe training breast mask images using the shape model. Additional details regarding operation of training shape registration unit  740  are described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0139]    Atlas output unit  750  receives from training shape registration unit  740  the results of shape registration for the set of training breast mask images analyzed. The set of training breast mask images have features that have been previously localized. Features could be cancer structures, benign structures, vessel areas, etc. Using shape registration results, the localized features in the training breast mask images are mapped from the training breast mask images onto the baseline breast atlas shape. An atlas is created with locations of the features in the baseline breast atlas shape. Since a large number of training breast mask images with previously localized features are used, the atlas is a probabilistic atlas that gives the probability for feature presence at each pixel inside the baseline breast atlas shape. One probabilistic atlas may be generated for each mammogram view. The probabilistic feature atlases for various breast views are sent to reference data unit  158 , to be used during operation of image processing unit  39 . Additional details regarding operation of atlas output unit  750  are described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference. 
         [0140]    Image operations unit  620 , baseline shape unit  710 , shape parameterization unit  720 , deformation analysis unit  730 , training shape registration unit  740 , atlas output unit  750 , image operations unit  128 , shape registration unit  138 , feature removal and positioning unit  148 , and probabilistic atlas reference unit  158  are software systems/applications. Image operations unit  620 , baseline shape unit  710 , shape parameterization unit  720 , deformation analysis unit  730 , training shape registration unit  740 , atlas output unit  750 , image operations unit  128 , shape registration unit  138 , feature removal and positioning unit  148 , and probabilistic atlas reference unit  158  may also be purpose built hardware such as FPGA, ASIC, etc. 
         [0141]    Methods and apparatuses disclosed in this application can be used for breast segmentation, artifact removal, mammogram view identification, nipple detection, etc. Methods and apparatuses disclosed in this application can be combined with methods and apparatuses disclosed in the co-pending non-provisional application titled “Method and Apparatus of Using Probabilistic Atlas for Cancer Detection”, the entire contents of which are hereby incorporated by reference, to perform breast segmentation, artifact removal, mammogram view identification, nipple detection, together with cancer detection for mammography images. Shape models and probabilistic atlases generated using techniques described in the co-pending non-provisional application titled “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, the entire contents of which are hereby incorporated by reference, can be used for breast segmentation, artifact removal, mammogram view identification, nipple detection, and cancer detection. Additional applications, such as temporal subtraction between breast images can be implemented using methods and apparatuses disclosed in this application, and methods and apparatuses disclosed in “Method and Apparatus of Using Probabilistic Atlas for Cancer Detection”. 
         [0142]    The methods and apparatuses disclosed in this application can be used for automatic detection of other features besides nipples in breasts. The methods and apparatuses can be used for feature removal, feature detection, feature positioning, and segmentation for other anatomical parts besides breasts, by using shape modeling techniques for the anatomical parts and atlases for locations of features in the anatomical parts. The methods and apparatuses disclosed in this application can be coupled with methods and apparatuses from “Method and Apparatus of Using Probabilistic Atlas for Cancer Detection” using shape models and probabilistic atlases generated as described in “Method and Apparatus for Probabilistic Atlas Based on Shape Modeling Technique”, to perform feature removal, feature detection, feature positioning, and object segmentation for other objects and anatomical objects besides breasts, and other features besides cancer structures or breast features. 
         [0143]    Although detailed embodiments and implementations of the present invention have been described above, it should be apparent that various modifications are possible without departing from the spirit and scope of the present invention.

Technology Classification (CPC): 6