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Adaptive fuzzy segmentation of magnetic resonance images

Pham, D.L., Prince, J.L.·1999·IEEE Transactions on Medical Imaging

Abstract

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D 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 inhomogeneities 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 lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

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Metadata

Authors
Pham, D. L., & Prince, J. L.
Venue
IEEE Transactions on Medical Imaging
Published
1999
Volume
18
Issue
9
Pages
737-752
ISSN
0278-0062 (ISSN-L)
OpenAlex
W2108707886
Cited by
805
References
1
FWCI
18.39
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Adaptive fuzzy segmentation of magnetic resonance images. https://app.cochat.ai/papers/10.1109/42.802752
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Pham, D. L., & Prince, J. L. (1999). Adaptive fuzzy segmentation of magnetic resonance images. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/42.802752

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