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Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05)
MRI Image Segmentation Using Unsupervised Clustering Techniques
Las Vegas, Nevada
August 16-August 18
ISBN: 0-7695-2358-7
D. Selvathi, MEPCO Schlenk Engineering College
A. Arulmurgan, MEPCO Schlenk Engineering College
S. Thamarai Selvi, Anna University
S. Alagappan, Devaki MRI & CT Scans
In medical image visualization and analysis, segmentation is an indispensable step in the processing of images. MR has become a particularly useful medical diagnostic tool for cases involving soft tissues, such as in brain imaging. The aim of our research is to develop an effective algorithm for the segmentation of the MRI images. This paper discusses the use and implementation of Fuzzy C Means Clustering and genetic algorithm (GA) for an automatic segmentation of White Matter (WM), Gray Matter (GM), Cerebro Spinal Fluid (CSF), the extra cranial regions and the presence of Tumor regions. The results were analyzed and compared with the reference gold standard obtained from radiologists.
Index Terms:
MR Imaging, Homomorphic Filtering, Segmentation, Fuzzy C Means, Genetic Algorithm
Citation:
D. Selvathi, A. Arulmurgan, S. Thamarai Selvi, S. Alagappan, "MRI Image Segmentation Using Unsupervised Clustering Techniques," iccima, pp.105-110, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2005
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