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

被引：374

作者：

Pham, DL

Prince, JL ^{[1
]}

机构：

[1] Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA

[2] NIA, Lab Personal & Cognit, GRC, NIH, Baltimore, MD 21224 USA

来源：

PATTERN RECOGNITION LETTERS | 1999年 / 20卷 / 01期

关键词：

image segmentation; fuzzy C-means; intensity inhomogeneities; magnetic resonance imaging;

D O I：

10.1016/S0167-8655(98)00121-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：57 / 68

页数：12

共 21 条

[1] REVIEW OF MR IMAGE SEGMENTATION TECHNIQUES USING PATTERN-RECOGNITION [J].