Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentation

被引：0
|
作者：
Biniaz, Abbas [1 ]
Abbassi, Ataollah [1 ]
Shamsi, Mousa [2 ]
Ebrahimi, Afshin [2 ]
机构：
[1] Sahand Univ Technol, Computat Neurosci Lab, Tabriz, Iran
[2] Sahand Univ Technol, Dept Elect Engn, Tabriz, Iran
来源：
2012 19TH IRANIAN CONFERENCE OF BIOMEDICAL ENGINEERING (ICBME) | 2012年
关键词：
component; Segmentation; MRI; FCM; spatial information;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Conventional fuzzy c-means (FCM) algorithm is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper aims to develop a Gaussian spatial FCM (gsFCM) for segmentation of brain magnetic resonance (MR) images. The proposed algorithm uses fuzzy spatial information to update fuzzy membership with a Gaussian function. Proposed method has less sensitivity to noise specifically in tissue boundaries, angles, and borders than spatial FCM (sFCM). Furthermore by the proposed algorithm a pixel which is a distinct tissue from anatomically point of view for example a tumor in preliminary stages of its appearance, has more chance to be a unique cluster. The quantitative assessment of presented FCM techniques is evaluated by conventional validity functions. Experimental results show the efficiency of proposed algorithm in segmentation of MR images.
引用
收藏
页码：132 / 136
页数：5
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