Abstract:
The identification of known normal structures within an image is preferably accomplished using an appearance model. Specifically, an active appearance model, which encapsulates a complete model of the shape and global texture variations of an object from a collection of samples, is utilized to define normal structures within an image by restricting training samples supplied to the active appearance model during a training phase to those that do not contain abnormal structures. Accordingly, the trained appearance model represents only normal variations in the object of interest. When another image with abnormalities is presented to the system, the appearance model cannot synthesize the abnormal structures which show up as errors in a residual image. Accordingly, the errors in the residual image represent potential abnormalities.

Description:
FIELD OF THE INVENTION 
       [0001]    The invention is directed to a method and apparatus for identifying abnormalities or anomalies within images. In particular, the invention is directed to a method and apparatus that utilizes an appearance model to identify abnormalities or anomalies within images. 
       BACKGROUND OF THE INVENTION 
       [0002]    Identification of abnormalities or anomalies in images is particularly useful in the field of medicine to diagnose a number of potentially serious and deadly medical conditions. The identification of abnormalities in medical images, such as conventional X-rays, magnetic resonance imaging (MRI) scans and computer tomography (CT) scans, initially relied upon the talents of a skilled clinician to view an image and manually identify the abnormalities within the image. As even the most advanced imaging technologies have become more readily available and affordable, however, the number of images generated for medical diagnostic purposes has increased dramatically over the years. It soon became apparent that automated methods and systems would have to be developed in order to decrease the amount of time required for a clinician to review an individual image. By prescreening images using automated methods and systems, a skilled clinician can review more images within a given time frame, which results in greater efficiency and an overall reduction in the expensive of reading and interpreting medical images. 
         [0003]    In view of the above, efforts have been made in the field of medical imaging technology to develop automated systems capable of imaging a particular area of the body and detecting potential abnormalities within the imaged area. For example, U.S. Pat. No. 7,103,224; entitled: “Method and System for Automatic Identification and Quantification of Abnormal Anatomical Structures in Medical Images”, by Edward Ashton; discloses a system that detects structures that are similar to an exemplar structure in order to detect lesions in images scans. In any such automated system, it is preferably to have identified abnormalities highlighted or otherwise visually indicated to allow a skilled clinician to quickly focus on areas of potential interest within an image. The skilled clinician can then identify whether the potential abnormality is significant and related to a potentially harmful medical condition or whether the potential abnormality is not significant and can be dismissed without further study. Thus, a great deal of the skilled clinician&#39;s time can be saved, even if the automated system does not positively identify abnormalities as harmful conditions, by quickly directing the clinician to those areas of the image most likely to contain abnormalities requiring the clinician&#39;s attention. 
         [0004]    In conventional imaging systems in which abnormalities are identified, the systems have generally focused on utilizing processes and techniques that identify structures associated with specific types of abnormalities such as tumors or lesions. Image processing techniques employed in conventional MRI&#39;s, for example, are often focused on identifying the specific morphology of tumors or other abnormal anatomical features within an image. Attempting to identify a structure of tumor, however, can be extremely difficult due to the wide range and variation in the morphology of tumors, making it difficult to create a robust system capable of detecting a wide range of abnormalities. Thus, automated systems based on identification and separation of abnormal structures from known normal structures tend to be complex in nature, as a great deal of effort must be made to model and define what an abnormal structure is compared with a normal structure. 
         [0005]    In view of the above, it would be desirable to provide a method and apparatus for identifying abnormalities within images that does not rely upon the identification of the structure of the abnormality itself. 
       SUMMARY OF THE INVENTION 
       [0006]    A method and apparatus for identifying abnormalities within images is provided that does not reply upon the identification of the structure of the abnormality itself. Put simply, instead of attempting to detect the specific structure of the abnormality within an image, the present invention identifies normal structures within the image and characterizes areas that cannot be identified as normal as potential abnormalities. The identification of normal structures can be accomplished in a much less complicated manner than attempting to identify the specific structure of an abnormality. 
         [0007]    The identification of known normal structures within an image is preferably accomplished by using an appearance model. Specifically, an active appearance model, which encapsulates a complete model of the shape and global texture variations of an object from a collection of samples, is utilized to define normal structures within an image by restricting training samples supplied to the active appearance model during a training phase to those that do not contain abnormal structures. Accordingly, the trained appearance model represents only normal variations in the object of interest. When another image with abnormalities is presented to the system, the appearance model cannot synthesize the abnormal structures which show up as errors in a residual image. Accordingly, the errors in the residual image represent potential abnormalities. Thus, the present invention identifies the normal structures within an image such that any unidentified structures are considered to be abnormalities which can then be reviewed in greater detail. 
         [0008]    In a preferred embodiment, a method of detecting abnormalities in an image is provided that includes supplying the image of the object to an image abnormality processing unit using a data entry interface device, synthesizing a sample normal image of the object based on an appearance model utilizing the image abnormality processing unit; and identifying abnormalities in the input image based at least upon an analysis of differences between the image of the object and the sample normal image of the object. The appearance model is preferably generated based on a set of training images. Preferably, a shape model and/or a texture model is applied to the set of training images to generate the appearance model. 
         [0009]    The method is preferably performed by an image abnormality identification system that includes a memory unit that stores an appearance model corresponding to a normal appearance of an object of interest, a data entry interface device for entering an input image of an object of interest, and an image abnormality processing unit that synthesizes a sample normal image of the object of interest based on the appearance model stored in the memory unit, and identifies abnormalities in the input image based at least upon an analysis of differences between the input image and the sample normal image of the object. 
         [0010]    Other features, advantages, modifications, embodiments, etc., will become apparent to those skilled in the art from the following detailed description of the preferred embodiments of the invention and the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    The invention will be described in detail with reference to certain preferred embodiments thereof and the accompany drawings, wherein: 
           [0012]      FIG. 1  is a schematic block diagram of an abnormality identification system in accordance with the claimed invention; 
           [0013]      FIG. 2  is a flow diagram illustrating a training phase used to train the abnormality identification system illustrated in  FIG. 1 ; and 
           [0014]      FIG. 3  is a flow diagram illustrating a testing phase performed by the abnormality identification system. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0015]      FIG. 1  is a schematic block diagram of an abnormality identification system  10  in accordance with the claimed invention. The abnormality identification system  10  preferably includes a data entry device  12  through which data is entered into the system, an image abnormality processing unit  14  that processes data received from the data entry device  12 , a memory unit  16  that stores data and/or executable instructions for the image abnormality processing unit  14 , and a display interface  18  that displays results to a user of the system. The data entry device  12  includes any desired type or combination of types of data entry devices, for example, magnetic media interface devices, optical media interface devices, hard-wired communication ports, wireless communication ports, etc., to permit data to be received and entered into the abnormality identification system  10 . The image abnormality processing unit  14  includes any desired type or combination of types of data processing devices, for example, application specific programmable devices, general purpose programmable processors, discrete hardware components, firmware, etc., necessary to perform the functions of the image abnormality processing unit  14 . The memory unit  16  includes any desired type or combination of types of memory devices for storing digital data, for example, magnetic and/or optical media drives or memory cards, semiconductor memory devices, etc., capable of performing the functions required of the memory unit  16 . Further, while elements of the abnormality identification system  10  are shown separately for purposes of illustration, the various components of the system may be combined in one or more physical devices. 
         [0016]    Prior to being able to process images for abnormality identification, the abnormality identification system  10  is first put through a training phase in order to generate a trained model of normal appearance as illustrated in  FIG. 2 . The training phase requires that a training set of normal images, which, in some embodiments, correspond to an evaluation object belonging to a predetermined class, be supplied to the image abnormality processing unit  14 . Depending upon the specific application, the training set of normal images may be stored in a remote image database  20  that can be accessed via a network  22  or may be stored on a portable storage medium. If the training set of normal images is stored in the remote image database  20 , the data entry device  12  connects to the remote image database  20  via the network  22  and downloads the training set of normal images to the abnormality identification system  10 . If the training set of normal images is contained on a portable storage medium, the portable storage medium is inserted into and read by the data entry device  12 . In either case, the training images are processed by the image abnormality processing unit  14 , either in real time as received by the data entry device  12  or after being downloaded to the memory unit  16  for later processing, to produce a trained model of normal appearance of the object. 
         [0017]    In a preferred embodiment, the image abnormality processing unit  14  utilizes both a texture model and a shape model to process the training images contained in the training set, although other types or combination of models may be employed. As will be readily understood by those skilled in the art, the term “model” in the current context refers to a set of executable instructions provided to the image abnormality processing unit  14  in order to perform a specific process. The texture model defines a texture distribution for a group of normal objects contained within the training set of images. The use of a texture model for image processing is well known in the art as discussed, for example, in the article entitled: “ A Parametric Texture Model based on Joint Statistics of Complex Wavelet Coefficients ”, by Javier Portilla and Eero P. Simoncelli, International Journal of Computer Vision 40(1), 49-71, 2000; and in the article entitled: “ Face - texture model based on SGLD and its application in face detection in a color scene ”, by Ying Dai and Yasuaki Nakano, Pattern Recognition 29(6):1007-1017, 1996; the content of each of which is incorporated herein by reference. The shape model defines a shape distribution for the group of normal objects. The use of shape models for image processing is also well known in the art as discussed in the article entitled: “ Search Strategies for Shape Regularized Active Contour ”, by Tianli Yu, Jiebo Luo, and Narendra Ahuja, Computer Vision and Image Understanding, 2008. The result of the application of the texture model and the shape model is a trained model of normal appearance that is stored in the memory unit  16 . The use of appearance models in image processing is also well known to those in the art as described, for example, in the article entitled: “ Active Appearance Models ”, by T. F. Cootes, G. J. Edwards, and C. J. Taylor, Proc. European Conference on Computer Vision 1998 (H. Burkhardt and B. Neumann, Eds.), Vol. 2, pp. 484-498, Springer, 1998; the content of which is incorporated herein by reference. 
         [0018]    In a preferred embodiment, the image abnormality processing unit  14  utilizes the steps shown in  FIG. 2  to build a trained model of normal appearance. The image abnormality processing unit  14  receives a set of training images (S 1 ) corresponding to the object class of a desired evaluation object. In some embodiments, a set of shape tuples (S 2 ) is created by extracting the shape contours of the normal objects in the set of training images. Similarly, in some embodiments, a set of texture tuples is created (S 3 ) by extracting the texture information associated with the normal objects in the set of training images. In a preferred embodiment, the set of texture tuples is created by first normalizing the shape contours of the objects in the set of training images to the mean shape contour of all objects in the set of training images. The result is a set of texture tuples that all have the same shape for the object (shape-free texture). In a preferred embodiment, principal component analysis (PCA), a technique well known in the art, is applied separately to the set of shape tuples and the set of texture tuples to create a model of normal shape variation (S 4 ) and to create a normal texture variation (S 5 ) for the desired evaluation object class. As will be readily understood by those skilled in the art, other techniques for creating the model of shape and texture variation may also be used. Finally, in a preferred embodiment, PCA is once again applied jointly to both the shape model and the texture model to create the trained model of normal appearance (S 6 ). 
         [0019]    The abnormality identification system  10  is ready to analyze images to identify potential abnormalities once the training phase has been completed and the model of normal appearance has been generated and stored in the memory unit  16 . A flow diagram of the testing phase is illustrated in  FIG. 3 . Images to be analyzed are entered through the data entry device  12  in the same manner as the training set of images. Upon receipt of an input image (S 7 ), the image abnormality processing unit  14  generates a synthesized image (S 9 ) from the trained model of normal appearance retrieved from the memory unit  16  (S 8 ). The synthesized image is then compared to the input image (S 10 ) to identify differences between the two images. A set of stopping criteria is calculated (S 11 ) based on these differences. In a preferred embodiment, the stopping criteria is a threshold on the amount of decrease in the error between two consecutive iterations of the image synthesis process. In another preferred embodiment, the stopping criteria is a threshold on the amount of error between the input image and the synthesized image. If the stopping criteria is not met, the image abnormality processing unit generates a new synthesized image based at least in part upon the differences so that the new error between the newly synthesized image and the original image is lesser than the previous differences. If the stopping criteria is met, then the areas of the input image that do not conform to the synthesized image are identified as areas that contain a potential abnormality. The areas containing the potential abnormalities, namely, the areas that don&#39;t correspond to normal areas or the normal object contained in the training set, are identified in a residual image (S 12 ) which can be stored in the memory unit  16  and displayed on the display interface  18 . In the case of a medical application, the areas containing potential abnormalities are preferably highlighted so that a skilled clinician can easily identify the areas requiring further review and study. 
         [0020]    The invention has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention. 
       PARTS LIST 
       [0000]    
       
           10  abnormality identification system 
           12  data entry device 
           14  image abnormality processing unit 
           16  memory unit 
           18  display interface 
           20  remote image database 
           22  network 
         PCA principal component analysis 
         S 1  training set (normal image only) 
         S 2  shape tuples 
         S 3  texture (shape-free) 
         S 4  model of shape 
         S 5  model of texture 
         S 6  trained model of normal appearance 
         S 7  input image 
         S 8  trained model of normal appearance 
         S 9  synthesize image from model 
         S 10  compare synthesized image to input image and compute error 
         S 11  stopping criteria met? 
         S 12  trained model of normal appearance