Conference paper
A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation
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.
Language: | English |
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Publisher: | Springer Berlin Heidelberg |
Year: | 2008 |
Pages: | 1092-1101 |
Proceedings: | International Symposium on Visual Computing |
ISBN: | 3540896384 , 3540896392 , 9783540896388 and 9783540896395 |
Types: | Conference paper |
DOI: | 10.1007/978-3-540-89639-5_104 |