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Proceedings Paper

Adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities
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Paper Abstract

We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy c-means algorithm to include a multiplier field, which allows the centroids for each class to vary across the image. First and second order regularization terms ensure that the multiplier field is both slowly varying and smooth. An iterative algorithm that minimizes the objective function is described, and its efficacy is demonstrated on several test images.

Paper Details

Date Published: 24 June 1998
PDF: 9 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310864
Show Author Affiliations
Dzung L. Pham, Johns Hopkins Univ. and National Institute of Health (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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