A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data

被引：56

作者：

He, Yanyan ^{[1
]}

Hussaini, M. Yousuff ^{[1
]}

Ma, Jianwei ^{[1
,4
]}

Shafei, Behrang ^{[2
]}

Steidl, Gabriele ^{[3
]}

机构：

[1] Florida State Univ, Dept Math, Tallahassee, FL 32306 USA

[2] Fraunhofer ITWM, Image Proc Dept, Image Proc Grp, Kaiserslautern, Germany

[3] Univ Kaiserslautern, Dept Math, D-67663 Kaiserslautern, Germany

[4] Harbin Inst Technol, Inst Appl Math, Harbin 150006, Peoples R China

来源：

PATTERN RECOGNITION | 2012年 / 45卷 / 09期

关键词：

Fuzzy c-means; Multi-class labeling; Sparsity-promoting method; Alternating direction method of multipliers; MRI segmentation; Noisy and incomplete data; ALGORITHM; OPTIMIZATION; FRAMEWORK; MODELS;

D O I：

10.1016/j.patcog.2012.03.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The objective function of the original (fuzzy) c-mean method is modified by a regularizing functional in the form of total variation (TV) with regard to gradient sparsity, and a regularization parameter is used to balance clustering and smoothing. An alternating direction method of multipliers in conjunction with the fast discrete cosine transform is used to solve the TV-regularized optimization problem. The new algorithm is tested on both synthetic and real data, and is demonstrated to be effective and robust in treating images with noise and missing data (incomplete data). (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：3463 / 3471

页数：9

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