Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/7917
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dc.contributor.authorArakeri, M.P.-
dc.contributor.authorRam Mohana Reddy, Guddeti-
dc.date.accessioned2020-03-30T10:03:05Z-
dc.date.available2020-03-30T10:03:05Z-
dc.date.issued2011-
dc.identifier.citationCommunications in Computer and Information Science, 2011, Vol.250 CCIS, , pp.790-795en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7917-
dc.description.abstractImage segmentation is one of the most vital and significant step in medical applications. The conventional fuzzy c-means (FCM) clustering is the most widely used unsupervised clustering method for brain tumor segmentation on magnetic resonance (MR) images. However, the major limitation of the conventional FCM is its huge computational time and it is sensitive to initial cluster centers. In this paper, we present a novel efficient FCM algorithm to eliminate the drawback of conventional FCM. The proposed algorithm is formulated by incorporating distribution of the gray level information in the image and a new objective function which ensures better stability and compactness of clusters. Experiments are conducted on brain MR images to investigate the effectiveness of the proposed method in segmenting brain tumor. The conventional FCM and the proposed method are compared to explore the efficiency and accuracy of the proposed method. � 2011 Springer-Verlag.en_US
dc.titleEfficient fuzzy clustering based approach to brain tumor segmentation on MR imagesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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