FCM Clustering Algorithms for Segmentation of Brain MR Images
Joint Authors
Dubey, Yogita K.
Mushrif, Milind M.
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-15
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues.
Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis.
Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries.
Therefore, accurate segmentation of brain images is still a challenging area of research.
This paper presents a review of fuzzy c -means (FCM) clustering algorithms for the segmentation of brain MR images.
The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness.
Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.
American Psychological Association (APA)
Dubey, Yogita K.& Mushrif, Milind M.. 2016. FCM Clustering Algorithms for Segmentation of Brain MR Images. Advances in Fuzzy Systems،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1095024
Modern Language Association (MLA)
Dubey, Yogita K.& Mushrif, Milind M.. FCM Clustering Algorithms for Segmentation of Brain MR Images. Advances in Fuzzy Systems No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1095024
American Medical Association (AMA)
Dubey, Yogita K.& Mushrif, Milind M.. FCM Clustering Algorithms for Segmentation of Brain MR Images. Advances in Fuzzy Systems. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1095024
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1095024