MammoSys: A content-based image retrieval system using breast density patterns.

In this paper, we present a content-based image retrieval system designed to retrieve mammographies from large medical image database. The system is developed based on breast density, according to the four categories defined by the American College of Radiology, and is integrated to the database of the Image Retrieval in Medical Applications (IRMA) project, that provides images with classification ground truth. Two-dimensional principal component analysis is used in breast density texture characterization, in order to effectively represent texture and allow for dimensionality reduction.

A contact map matching approach to protein structure similarity analysis.

We modeled the problem of identifying how close two proteins are structurally by measuring the dissimilarity of their contact maps. These contact maps are colored images, in which the chromatic information encodes the chemical nature of the contacts. We studied two conceptually distinct image-processing algorithms to measure the dissimilarity between these contact maps; one was a content-based image retrieval method, and the other was based on image registration.