An algorithm is proposed for the fuzzy segmentation of two and three-dimensional multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the two-dimensional adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomo-geneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new, faster multigrid-based algorithm for its implementation. We show using simulated MR data that 3-D AFCM yields significantly lower error rates than both the standard fuzzy C—means algorithm and several other competing methods when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.
CITATION STYLE
Pham, D. L., & Prince, J. L. (1999). An adaptive fuzzy segmentation algorithm for three-dimensional magnetic resonance images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1613, pp. 140–153). Springer Verlag. https://doi.org/10.1007/3-540-48714-x_11
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