Homogeneous Patch Based FCM Algorithm for Brain MR Image Segmentation

被引：0

作者：

Chen, Qiang ^{[1
]}

Ji, Zexuan ^{[1
]}

Sun, Quansen ^{[1
]}

Xia, Deshen ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Peoples R China

来源：

PROCEEDINGS OF THE 2009 CHINESE CONFERENCE ON PATTERN RECOGNITION AND THE FIRST CJK JOINT WORKSHOP ON PATTERN RECOGNITION, VOLS 1 AND 2 | 2009年

关键词：

Fuzzy c-means clustering; image segmentation; homogeneous patch; brain MR image; C-MEANS ALGORITHM; FUZZY CLUSTERING-ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a homogeneous patch based fuzzy c-means (FCM) clustering algorithm for brain magnetic resonance (MR) image segmentation. Currently, FCM is mainly improved by incorporating local spatial information for noise immunity. The proposed algorithm is based on image patch space, which can avoid introducing an extra control parameter for local spatial restriction. In order to decrease the edge blurring caused by local spatial restriction, the local polynomial approximation-intersection of confidence intervals (LPA-ICI) technique is used to construct the homogeneous patch. Brain MR image segmentation results indicate that the proposed algorithm is better than the other improved FCM algorithms that incorporate local spatial information, while the detail preservation need to be improved.

引用

页码：593 / 597

页数：5

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