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dc.contributor.authorRavi, A.-
dc.contributor.authorSuvarna, A.-
dc.contributor.authorD'Souza, A.-
dc.contributor.authorRam Mohana Reddy, Guddeti-
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2013, Vol.174 AISC, , pp.787-794en_US
dc.description.abstractThe Fuzzy C Means (FCM) algorithm has been extensively used in medical image segmentation. But for large data sets the convergence of the FCM algorithm is time consuming and also requires considerable amount of memory. In some real time applications, like Content Based Medical Image Retrieval (CBIR) systems, there is a need to segment a large volume of brain MRI images offline. In this paper, we present an efficient method to cluster data points of all the images at once. The gray level histogram is used in the FCM algorithm to minimize the time for segmentation and the space required. A parallel approach is then applied to further reduce the computation time. The proposed method is found to be almost twice as fast as conventional FCM. � 2013 Springer.en_US
dc.titleA parallel fuzzy C means algorithm for brain tumor segmentation on multiple MRI imagesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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