Share Email Print

Proceedings Paper

Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques
Author(s): Anke Meyer-Baese; Axel Wismueller M.D.; Oliver Lange; Gerda Leinsinger
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammography as a first step of a computer-aided diagnosis system. Three new unsupervised clustering techniques are tested on biomedical time-series representing breast MRI scans: fuzzy clustering based on deterministic annealing, "neural gas" network, and topographic independent component analysis. While the first two methods enable a correct segmentation of the lesion, the latter, although incorporating a topographic mapping, fails to detect and subclassify lesions.

Paper Details

Date Published: 12 April 2004
PDF: 9 pages
Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); doi: 10.1117/12.542249
Show Author Affiliations
Anke Meyer-Baese, Florida State Univ. (United States)
Axel Wismueller M.D., Florida State Univ. (United States)
Ludwig-Maximilians-Univ. Munchen (Germany)
Oliver Lange, Florida State Univ. (United States)
Ludwig-Maximilians-Univ. Munchen (Germany)
Gerda Leinsinger, Ludwig-Maximilians-Univ. München (Germany)

Published in SPIE Proceedings Vol. 5421:
Intelligent Computing: Theory and Applications II
Kevin L. Priddy, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?