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

Dzung L. Pham; Jerry L. Prince

doi:10.1117/12.310864

24 June 1998 Adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities

Dzung L. Pham, Jerry L. Prince

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310864
Event: Medical Imaging '98, 1998, San Diego, CA, United States

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.

Citation Download Citation

Dzung L. Pham and Jerry L. Prince "Adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310864

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