You are not logged in.

Comparison of 3S multi-thresolding with fuzzy c-means method

Mortazavi, Daryoush, Mashohor, Syamsiah, Mahmud, Rozi and Jantan, Adznan B. 2009, Comparison of 3S multi-thresolding with fuzzy c-means method, in CITISIA 2009 : Proceedings of the 2009 Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, IEEE Computer Society, Los Alamitos, Calif., pp. 119-124, doi: 10.1109/CITISIA.2009.5224230.

Attached Files
Name Description MIMEType Size Downloads

Title Comparison of 3S multi-thresolding with fuzzy c-means method
Author(s) Mortazavi, Daryoush
Mashohor, Syamsiah
Mahmud, Rozi
Jantan, Adznan B.
Conference name Innovative Technologies in Intelligent Systems and Industrial Applications. Conference (2009 : Serdang, Malaysia)
Conference location Serdang, Malaysia
Conference dates 25-26 Jul. 2009
Title of proceedings CITISIA 2009 : Proceedings of the 2009 Conference on Innovative Technologies in Intelligent Systems and Industrial Applications
Editor(s) [Unknown]
Publication date 2009
Conference series Innovative Technologies in Intelligent Systems and Industrial Applications. Conference
Start page 119
End page 124
Total pages 6
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) biomedical imaging
clustering algorithms
clustering methods
entropy
image converters
image segmentation
intelligent systems
lesions
magnetic resonance imaging
steady-state
Summary The 3S (Shrinking-Search-Space) multi-thresholding method which have been used for segmentation of medical images according to their intensities, now have been implemented and compared with FCM method in terms of segmentation quality and segmentation time as a benchmark in thresholding. The results show that 3S method produced almost the same segmentation quality or in some occasions better quality than FCM, and the computation time of 3S method is much lower than FCM. This is another superiority of this method with respect to others. Also, the performance of C-means has been compared with two other methods. This comparison shows that, C-means is not a reliable clustering algorithm and it needs several run to give us a reliable result.
ISBN 1424428866
9781424428861
Language eng
DOI 10.1109/CITISIA.2009.5224230
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049222

Document type: Conference Paper
Collection: School of Engineering
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 44 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Thu, 01 Nov 2012, 13:11:41 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.