Title :
A Fuzzy Clustering Technique for Medical Image Segmentation
Author_Institution :
Inst. of Appl. Informatics, Wroclaw Univ. of Technol.
Abstract :
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
Keywords :
feature extraction; fuzzy set theory; image segmentation; medical image processing; pattern clustering; Euclidean metric; anatomical structure; classical fuzzy c-means clustering; feature extraction; fuzzy similarity relation; medical image segmentation; Anatomical structure; Biomedical imaging; Clustering algorithms; Clustering methods; Computed tomography; Data mining; Fuzzy systems; Image segmentation; Machine learning algorithms; Pixel;
Conference_Titel :
Evolving Fuzzy Systems, 2006 International Symposium on
Conference_Location :
Ambleside
Print_ISBN :
0-7803-9718-5
Electronic_ISBN :
0-7803-9719-3
DOI :
10.1109/ISEFS.2006.251140
