Spatial models for fuzzy clustering

被引：322

作者：

Pham, DL ^{[1
]}

机构：

[1] NIA, Lab Personal & Cognit, Gerontol Res Ctr, NIH, Baltimore, MD 21224 USA

来源：

COMPUTER VISION AND IMAGE UNDERSTANDING | 2001年 / 84卷 / 02期

关键词：

fuzzy clustering; fuzzy c-means; image segmentation; Markov random fields; cross-validation;

D O I：

10.1006/cviu.2001.0951

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel approach to fuzzy clustering for image segmentation is described. The fuzzy C-means objective function is generalized to include a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy C-means algorithm and allows the estimation of spatially smooth membership functions. To determine the strength of the penalty function, a criterion based on cross-validation is employed. The new algorithm is applied to simulated and real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches. (C) 2001 Elsevier Science (USA).

引用

页码：285 / 297

页数：13

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