Mammographic Region of Interest Database Retrieval and Indexing Engine

Abstract

Computer Aided Diagnosis (CAD) tools for classification of benign and malignant breast lesions are often applied and used as a “second opinion” to reduce misclassification rate. Most CAD tools rely rigidly on extraction of discriminatory feature sets. However, radiologist’s reasoning about mammographic meaning is often based on complex visual and non-visual characteristic aspects that are difficult to express in suitable discriminatory feature based representations. This is due to the fact that the interpretation of imagery data by a human involves various cognitive processes that are too complex to formulate in an algorithmic way. Consequently, in applying CAD solutions it is important for radiologists to realize how the CAD tool can negatively alter their visual cognitive reasoning. To address the above concern, we have developed an approach that seeks to aid radiologists by providing indexing and retrieval schemas based on contents-based information addressing rather than just the “second opinion” CAD tool. In this approach, a trained hybrid classifier is used to return a gallery of “similar” cases, represented as regions of interest (ROI), with their ground truth, i.e., ROI annotation, full mammogram, and/or biopsy results. By examining these similar cases and using recognition heuristics the radiologist makes his/her final determination.