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.
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Van Luong, H., Kim, J.M. (2008). A New Parallel Approach to Fuzzy Clustering for Medical Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_104
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DOI: https://doi.org/10.1007/978-3-540-89639-5_104
Publisher Name: Springer, Berlin, Heidelberg
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