Paper
1 July 1992 Partially supervised fuzzy c-means algorithm for segmentation of MR images
Amine M. Bensaid, James C. Bezdek, Lawrence O. Hall, Robert Paul Velthuizen, Laurence P. Clarke
Author Affiliations +
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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Amine M. Bensaid, James C. Bezdek, Lawrence O. Hall, Robert Paul Velthuizen, and Laurence P. Clarke "Partially supervised fuzzy c-means algorithm for segmentation of MR images", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140120
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Cited by 15 scholarly publications.
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KEYWORDS
Image segmentation

Tissues

Magnetic resonance imaging

Tumors

Detection and tracking algorithms

Image classification

Fuzzy logic

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