The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Fast and Robust Fuzzy C-Means Algorithms for Automated Brain MR Image Segmentation
|
|
Author(s): László Szilágyi (Hungarian Science University of Transylvania, Romania), Sándor Miklós Szilágyi (Hungarian Science University of Transylvania, Romania)and Zoltán Benyó (Budapest University of Technology and Economics, Hungary)
Copyright: 2008
Pages: 9
Source title:
Encyclopedia of Healthcare Information Systems
Source Author(s)/Editor(s): Nilmini Wickramasinghe (Illinois Institute of Technology, USA)and Eliezer Geisler (Illinois Institute of Technology, USA)
DOI: 10.4018/978-1-59904-889-5.ch073
Purchase
|
Abstract
By definition, image segmentation represents the partitioning of an image into nonoverlapping, consistent regions, which appear to be homogeneous with respect to some criteria concerning gray level intensity and/or texture. The fuzzy c-means (FCM) algorithm is one of the most widely used method for data clustering, and probably also for brain image segmentation (Bezdek & Pal., 1991). However, in this latter case, standard FCM is not efficient by itself, as it is unable to deal with that relevant property of images that neighbor pixels are strongly correlated. Ignoring this specificity leads to strong noise sensitivity and several other imaging artifacts.
Related Content
|
.
© 2024.
27 pages.
|
|
.
© 2024.
10 pages.
|
|
.
© 2024.
13 pages.
|
|
.
© 2024.
6 pages.
|
|
.
© 2024.
23 pages.
|
|
.
© 2024.
14 pages.
|
|
.
© 2024.
7 pages.
|
|
|