Method and system of classifying medical images

Abstract

A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.

What is claimed is: 
     
         1 . A computerized method of classifying a medical image using a category model, comprising:
 using at least one processing unit for executing code instructions for:
 receiving an examined medical image; 
 identifying image coordinates of a plurality of image patches in said examined medical image, each image patch is represented by a plurality of repeatable multidimensional features in a pixel area of said examined medical image; 
 providing a category model which maps a plurality of visual-words in a space, each said visual-word is represented by a plurality of reference repeatable multidimensional features in a reference pixel area and image coordinates indicative of a location of said reference pixel area in said space and is associated with at least one of a plurality of image categories; 
 using said plurality of image patches and said image coordinates of a plurality of image patches to identify a group of said plurality of visual-words in said examined medical image; and 
 categorizing a pathology in said examined medical image according to said group. 
   
     
     
         2 . The computerized method of  claim 1 , presenting said pathology in a client terminal used to provide said examined medical image. 
     
     
         3 . The computerized method of  claim 1 , wherein said group is identified without segmenting said examined medical image. 
     
     
         4 . The computerized method of  claim 1 , wherein said group is identified is performed without registering said examined medical image. 
     
     
         5 . The computerized method of  claim 1 , further comprising updating said category model according to said pathology. 
     
     
         6 . The computerized method of  claim 1 , wherein said category model comprises less than 700 visual words. 
     
     
         7 . The computerized method of  claim 1 , wherein said category model is generated by an analysis of a training set having more than 10,000 medical images. 
     
     
         8 . The computerized method of  claim 1 , wherein said category model is generated by clustering a plurality of image patches from a plurality of medical images in a plurality of clusters, said plurality of visual words being defined according to said plurality of clusters. 
     
     
         9 . The computerized method of  claim 8 , wherein said clustering is performed according to a principal component analysis (PCA). 
     
     
         10 . The computerized method of  claim 8 , wherein said plurality of medical images are provided from a picture archiving communication system (PACS). 
     
     
         11 . The computerized method of  claim 1 , wherein said category model is modeled using a support vector machine (SVM) training procedure. 
     
     
         12 . The computerized method of  claim 11 , wherein said SVM training procedure is a multi-class SVM with a radial basis function (RBF) kernel. 
     
     
         13 . The computerized method of  claim 1 , wherein the category model is updated upon each usage of the category model. 
     
     
         14 . The computerized method of  claim 1 , further comprising normalizing each image patch, wherein each normalized image patch is formed from a transformation of intensity values from a corresponding image patch, to render the image patch less variant to brightness, and to provide local contrast enhancement. 
     
     
         15 . The computerized method of  claim 14 , wherein said intensity values from the image patch are obtained from pixels of the image patch. 
     
     
         16 . The computerized method of  claim 1 , wherein said repeatable multidimensional features in each said image are from three dimensional images. 
     
     
         17 . The computerized method of  claim 1 , further comprising outputting said category model for facilitating the categorization of an image based on local descriptors thereof including said image from three dimensional images. 
     
     
         18 . The computerized method of  claim 1 , wherein the pathology is selected from the group consisting of enlarged heart, lung infiltrates, right pleural effusion and left pleural effusion. 
     
     
         19 . A system of classifying a medical image using a category model, comprising:
 an interface adapted for receiving an examined medical image;   a memory adapted to store a category model which maps a plurality of visual-words in a space, each said visual-word is represented by a plurality of reference repeatable multidimensional features in a reference pixel area and image coordinates indicative of a location of said reference pixel area in said space and is associated with at least one of a plurality of image categories;   a code store adapted for a code;   at least one processing unit for executing said code;   wherein said code comprising:
 code instructions for identifying image coordinates of a plurality of image patches in said examined medical image, each image patch is represented by a plurality of repeatable multidimensional features in a pixel area of said examined medical image; 
 code instructions for using said plurality of image patches and said image coordinates of a plurality of image patches to identify a group of said plurality of visual-words in said examined medical image; and 
 code instructions for categorizing a pathology in said examined medical image according to said group.