Partially supervised clustering for image segmentation

Authors:
Amine M. Bensaid;Lawrence O. Hall;James C. Bezdek;Laurence P. Clarke
Affiliations:
Computer Science and Math Division, Al Akhawayn University in Ifrane (AUI), P.O. Box 104, Ave. Hassan II, Ifrane 53000, Morocco;Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, U.S.A.;Department of Computer Science, University of West Florida, Pensacola, FL 32514, U.S.A.;Department of Radiology, University of South Florida, Tampa, FL 33620, U.S.A.
Venue:
Pattern Recognition
Year:
1996

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Abstract

All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tendency to recommend solutions that equalize cluster populations. The semi-supervised c-means algorithms introduced in this paper attempt to overcome these problems domains where a few data from each clas can be labeled. Segmentation of magnetic resonance images is a problem of this type and we use it to illustrate the new algorithm. Our examples show that the semi-supervised approach provides MRI segmentations that are superior to ordinary fuzzy c-means and to the crisp k-nearest neighbor rule and further, that the new method ameliorates (P1)-(P3).