Patent ID: 7693334

Claim:
A pathological diagnosis support device, comprising: learning pattern input means for obtaining images centered on a tumor from a pathological image and inputting thereto the images as learning patterns; learning pattern storage means for storing and keeping the learning patterns to which class information is attached; feature candidate generator means for generating a plurality of feature candidates; feature determining means for determining a feature set of features suitable for diagnosis using the feature candidates generated by the feature candidate generator means; feature storage means for storing and keeping the set of features determined by the feature determining means; category table generator means for generating a category table; pattern input means for obtaining, from a pathological image to be diagnosed, images centered on a tumor candidate and inputting the images as input patterns; feature extracting means for extracting features from the input patterns; and diagnosis means for conducting diagnosis using the features, wherein: the feature determining means calculates a feature of each of the learning patterns corresponding to each of the feature candidates and determines as a first feature of the feature set, a feature candidate for which a mutual information quantity with respect to the class information of a set of the learning patterns takes a maximum value; and sequentially determines, under a condition that the determined feature is known, as a subsequent feature of the feature set, a feature candidate for which mutual information quantity between a feature of each learning pattern corresponding to each feature candidate and the class information of an associated one of the learning patterns takes a maximum value; the category table generator means calculates each feature of each of the learning patterns using the feature set and classifies the patterns using the category table including each feature of the learning patterns and the class information; and the feature extracting means calculates each feature of the input patterns using the feature set, wherein the learning pattern input means and the pattern input means select, from R, G, and B values of each pixel in the pathological image stained in advance, pixels belonging to a color region to which a cell nucleus of a predetermined tumor belongs, calculate distance between a center of distribution of the color region and each pixel belonging to the color region, assign a signal to the pixel according to the distance, detect a peak of distribution of the signals in the pathological image, and input an image centered on the peak as the learning pattern.