A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation

Huynh Van Luong, Jong Myon Kim

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Medical image segmentation plays an important role in medical image analysis and visualization. The Fuzzy c-Means (FCM) is one of the well-known methods in the practical applications of medical image segmentation. FCM, however, demands tremendous computational throughput and memory requirements due to a clustering process in which the pixels are classified into the attributed regions based on the global information of gray level distribution and spatial connectivity. In this paper, we present a parallel implementation of FCM using a representative data parallel architecture to overcome computational requirements as well as to create an intelligent system for medical image segmentation. Experimental results indicate that our parallel approach achieves a speedup of 1000x over the existing faster FCM method and provides reliable and efficient processing on CT and MRI image segmentation.
Original languageEnglish
Title of host publicationAdvances in Visual Computing : 4th International Symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008. Proceedings, Part I
PublisherSpringer
Publication date2008
Pages1092-1101
ISBN (Print)978-3-540-89638-8
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event4th International Symposium on Advances in Visual Computing (ISVC 2008) - Las Vegas, NV, United States
Duration: 1 Dec 20083 Dec 2008
http://isvc.net/

Conference

Conference4th International Symposium on Advances in Visual Computing (ISVC 2008)
CountryUnited States
CityLas Vegas, NV
Period01/12/200803/12/2008
Internet address
SeriesLecture Notes in Computer Science
Volume5358
ISSN0302-9743

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