An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities

Dzung L. Pham; Jerry L. Prince

doi:10.1016/S0167-8655(98)00121-4

An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities

Dzung L. Pham, Jerry L. Prince

Whiting School of Engineering

Research output: Contribution to journal › Article › peer-review

399 Scopus citations

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.

Original language	English (US)
Pages (from-to)	57-68
Number of pages	12
Journal	Pattern Recognition Letters
Volume	20
Issue number	1
DOIs	https://doi.org/10.1016/S0167-8655(98)00121-4
State	Published - Jan 1999

Keywords

Fuzzy C-means
Image segmentation
Intensity inhomogeneities
Magnetic resonance imaging

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/S0167-8655(98)00121-4

Cite this

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AB - 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.

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KW - Image segmentation

KW - Intensity inhomogeneities

KW - Magnetic resonance imaging

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An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities

Abstract

Keywords

ASJC Scopus subject areas

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