A fuzzy clustering technique for medical image segmentation

被引：10
|
作者：
Tabakov, Martin [1 ]
机构：
[1] Wroclaw Univ Technol, Inst Appl Informat, PL-50370 Wroclaw, Poland
来源：
2006 INTERNATIONAL SYMPOSIUM ON EVOLVING FUZZY SYSTEMS, PROCEEDINGS | 2006年
关键词：
D O I：
10.1109/ISEFS.2006.251140
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The main objective of medical image segmentation is to extract and characterise anatomical structures with respect to some input features or expert knowledge. This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Euclidean metric. A fuzzy similarity relation - based image segmentation algorithm is also introduced. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. Some results, using the classical fuzzy c - means clustering algorithm are also presented, for a comparison purpose.
引用
收藏
页码：118 / 122
页数：5
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