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Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-04
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one.
The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images.
The proposed method first removes the skull region using a skull stripping algorithm.
Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images.
As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images.
Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues.
Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches.
According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.
American Psychological Association (APA)
Chen, Yao-Tien. 2017. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-21.
https://search.emarefa.net/detail/BIM-1190835
Modern Language Association (MLA)
Chen, Yao-Tien. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering. Mathematical Problems in Engineering No. 2017 (2017), pp.1-21.
https://search.emarefa.net/detail/BIM-1190835
American Medical Association (AMA)
Chen, Yao-Tien. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-21.
https://search.emarefa.net/detail/BIM-1190835
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190835