Application of the fuzzy c-means clustering algorithm on the analysis of medical images

被引：0
|
作者：
Tian, Jie [1 ]
Han, Bo-Wen [1 ]
Wang, Yan [1 ]
Luo, Xi-Ping [1 ]
机构：
[1] Artificial Intelligent Lab., Inst. of Automat., Chinese Acad. of Sci., Beijing 100080, China
来源：
Ruan Jian Xue Bao/Journal of Software | 2001年 / 12卷 / 11期
关键词：
Algorithms - Bone - Fuzzy sets - Image segmentation - Mathematical models - Three dimensional;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
An improved method is proposed based on the Fuzzy C-means method to deal with medical images. This method includes three steps. The first step is the fuzzy pixels process in which a redundant image is built by FEV (fuzzy expectation value). The second step is the procession of FCM (fuzzy C-means clustering) with original images and their redundant images. The last step is the display of 3D model. This algorithm improves the accuracy of clustering as the redundant image increases the features of pixels. Several results of medical images are exhibited including CT, spiral CT and MRI, which are processed with the 3D MIPA system developed by the authors. Because better segmentation results are obtained, the system can clearly represent the anatomy structure of bones and the bones in the joint based on recognition and 3D reconstruction.
引用
收藏
页码：1623 / 1629
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