Medical brain MRI images segmentation by improved fuzzy C-Means clustering analysis

被引：0
|
作者：
Zhou, Xian-Guo [1 ]
Chen, Da-Ke [2 ]
Yuan, Sen-Miao [2 ]
机构：
[1] Peoplés Hospital of Jilin Province, Changchun 130022, China
[2] College of Communication Engineering, Jilin University, Changchun 130022, China
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2009年 / 39卷 / SUPPL. 2期
关键词：
Fuzzy clustering - Medical imaging - Clustering algorithms - Image enhancement - Magnetic resonance imaging - Graphic methods - Iterative methods;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
For the shortcomings of huge calculation in MRI images segmentation with standard Fuzzy C-Mean algorithm (FCM), a new algorithm combined with histogram statistical information of Improved Fast Fuzzy C-Mean algorithm (HF-KFCM) was proposed. Firstly, the method of multi-scale window traverse is used by this algorithm to find the histogram peaks. Then these peaks are defined as the fuzzy clustering initialization centre. Meanwhile, the fast FCM method based on histogram statistic is used as ergodicity to reduce each iteration calculation. The simulation results show that compared with standard FCM algorithm and other improved algorithms, the proposed algorithm can be improved significantly in fuzzy and clustering effectiveness.
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收藏
页码：381 / 385
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