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Proceedings Paper

Partially supervised fuzzy c-means algorithm for segmentation of MR images
Author(s): Amine M. Bensaid; James C. Bezdek; Lawrence O. Hall; Robert Paul Velthuizen; Laurence P. Clarke
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Paper Abstract

Partial supervision is introduced to the unsupervised fuzzy c-means algorithm (FCM). The resulting algorithm is called semi-supervised fuzzy c-means (SFCM). Labeled data are used as training information to improve FCM's performance. Training data are represented as training columns in SFCM's membership matrix (U), and are allowed to affect the cluster center computations. The degree of supervision is monitored by choosing the number of copies of the training set to be used in SFCM. Preliminary results of SFCM (applied to MRI segmentation) suggest that FCM finds the clusters of most interest to the user very accurately when training data is used to guide it.

Paper Details

Date Published: 1 July 1992
PDF: 7 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140120
Show Author Affiliations
Amine M. Bensaid, Univ. of South Florida (United States)
James C. Bezdek, Univ. of West Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Robert Paul Velthuizen, Univ. of South Florida (United States)
Laurence P. Clarke, Univ. of South Florida (United States)

Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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