DocumentCode
2090686
Title
A Novel Modified Kernel Fuzzy C-Means Clustering Algorithm on Image Segementation
Author
Yu, Chun-yan ; Li, Ying ; Liu, Ai-lian ; Liu, Jing-hong
Author_Institution
Inst. of Inf. & Sci. Technol., Dalian Maritime Univ., Dalian, China
fYear
2011
fDate
24-26 Aug. 2011
Firstpage
621
Lastpage
626
Abstract
Image segmentation plays an important role in imaging analysis. Based on the Mercer kernel, the fuzzy kernel c-means clustering algorithm (FKCM) is derived from the fuzzy c-means clustering algorithm (FCM).The FKCM algorithm that provides image clustering can improve accuracy significantly compared with classical fuzzy c-Means algorithms. In this paper, considering the advantages of KFCM, we propose a novel modified kernel fuzzy c means(NMKFCM) algorithm based on conventional KFCM which incorporates the neighbor term into its objective function. The results of experiments performed on synthetic and real medical images show that the new algorithm is effective and efficient, and has better performance in noisy images.
Keywords
fuzzy reasoning; fuzzy set theory; image segmentation; medical image processing; pattern clustering; FKCM algorithm; Mercer kernel; image clustering; image segmentation; imaging analysis; modified kernel fuzzy c-means clustering algorithm; noisy image; objective function; real medical image; synthetic medical image; Accuracy; Algorithm design and analysis; Biomedical imaging; Clustering algorithms; Image segmentation; Kernel; Noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Science and Engineering (CSE), 2011 IEEE 14th International Conference on
Conference_Location
Dalian, Liaoning
Print_ISBN
978-1-4577-0974-6
Type
conf
DOI
10.1109/CSE.2011.109
Filename
6062941
Link To Document