A study of mammographic mass retrieval based on shape and texture descriptors

Zhengdong Zhou; Fengmei Zou; Kwabena Agyepong

doi:10.1117/12.770923

17 March 2008 A study of mammographic mass retrieval based on shape and texture descriptors

Zhengdong Zhou, Fengmei Zou, Kwabena Agyepong

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69152B (2008) https://doi.org/10.1117/12.770923
Event: Medical Imaging, 2008, San Diego, California, United States

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.

Citation Download Citation

Zhengdong Zhou, Fengmei Zou, and Kwabena Agyepong "A study of mammographic mass retrieval based on shape and texture descriptors", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152B (17 March 2008); https://doi.org/10.1117/12.770923

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available