Classification-driven pathological neuroimage retrieval using statistical asymmetry measures

Abstract

This paper reports our methodology and initial results on volumetric pathological neuroimage retrieval. A set of novel image features are computed to quantify the statistical distributions of approximate bilateral asymmetry of normal and pathological human brains. We apply memory-based learning method to find the most-discriminative feature subset through image classification according to predefined semantic categories. Finally, this selected feature subset is usedas indexing features to retrieve medically similar images under a semantic-based image retrieval framework. Quantitative evaluations are provided.