A Study of Mammographic Mass Retrieval Based on Shape and Texture Descriptors

Content-based mass image retrieval technology, utilizing both shape and texture features, is investigated in this paper. In order to retrieve similar mass patterns that help improve clinical diagnosis, the performance of mass retrieval using curvature scale space descriptors (CSSDs) and R-transform descriptors was mainly studied. The mass contours in the DDSM database (Univ. of South Florida) were preprocessed to eliminate curl cases, which is very important for the extraction of features. The peak extraction method from a CSS contour map by circular shift and CSSDs matching method were introduced. Preliminary experiments show that the performance of CSSDs and R-transform descriptors outperform other features such as moment invariants, normalized Fourier descriptors (NFDs), and the combined texture feature. By combining CSSDs with R-transform descriptors and the texture features based on Gray-level Co-occurrence Matrices (GLCMs), the experiments show that the hybrid method gives a better performance in mass image retrieval than CSSDs or R-transform descriptors.