Title :
A Novel Segmentation Method for CT Head Images Using PSFCM-ES
Author :
Wei, Kaiping ; He, Bin ; Zhang, Tao
Author_Institution :
Dept. of Comput. Sci., Huazhong Normal Univ., Wuhan
Abstract :
With an expert system, a novel fuzzy c-means clustering method based on PSO and expert system (PSFCM-ES) is proposed in this paper. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. The k-nearest neighbor (k-NN) algorithm is introduced for calculating the weight in the spatially weighted FCM algorithm so as to improve the performance of image clustering. To speed up the FCM algorithm, the iteration is carried out with the gray level histogram of image instead of the conventional whole data of image. PSO algorithm is included to select optimal cluster centers and expert system is also introduced to solve the labeling problems. Experimental results indicate the proposed approach is effective and efficient.
Keywords :
computerised tomography; diagnostic radiography; fuzzy set theory; image segmentation; iterative methods; medical expert systems; medical image processing; particle swarm optimisation; pattern clustering; CT head images; PSFCM-ES; PSO; expert system; fuzzy c-means clustering method; gray level histogram; image clustering; iteration method; k-nearest neighbor; labeling problems; optimal cluster centers; segmentation method; spatial neighborhood information; Clustering algorithms; Clustering methods; Computed tomography; Expert systems; Fuzzy systems; Head; Histograms; Hybrid intelligent systems; Image segmentation; Labeling;
Conference_Titel :
Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1747-6
Electronic_ISBN :
978-1-4244-1748-3
DOI :
10.1109/ICBBE.2008.822