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Proceedings Paper

A study of mammographic mass retrieval based on shape and texture descriptors
Author(s): Zhengdong Zhou; Fengmei Zou; Kwabena Agyepong
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Paper Abstract

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.

Paper Details

Date Published: 17 March 2008
PDF: 11 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152B (17 March 2008); doi: 10.1117/12.770923
Show Author Affiliations
Zhengdong Zhou, Alcorn State Univ. (United States)
Nanjing Univ. of Aeronautics and Astronautics (China)
Fengmei Zou, Alcorn State Univ. (United States)
Xiamen Univ. (China)
Kwabena Agyepong, Alcorn State Univ. (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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