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contributor.authorPradhan, Smita-
contributor.authorPatra, D-
date.accessioned2010-04-26T11:01:53Z-
date.available2010-04-26T11:01:53Z-
date.issued2009-
identifier.citationIEEE India Council Conference, INDICON 2009; Ahmedabad; 18 December 2009 through 20 December 2009; Category number CFP09598; Code 79708; Article number 5409417en
identifier.urihttp://dx.doi.org/10.1109/INDCON.2009.5409417-
identifier.urihttp://hdl.handle.net/2080/1235-
description.abstractIn this paper, image segmentation of brain magnetic resonance (MR) image is addressed in an unsupervised framework. We propose a novel method considering the hidden Markov random field model (HMRF) to model the image class labels, which takes into account the mutual influences of neighbouring sites formulated on the basis of fuzzy clustering principle. By introducing the effective means to incorporate the explicit assumptions of the HMRF model into fuzzy clustering procedure, an efficient fuzzy clustering- type treatment is yielded. This combines the benefits from the spatial coherency modelling capabilities of the HMRF model, and the enhanced flexibility obtained by the fuzzy clustering algorithm, i.e. fuzzy c-means algorithm (FCM). The proposed HMRF-FCM segmentation framework is validated with noisy synthesis as well as brain MR images. We experimentally demonstrate the superiority of the proposed approach over the existing HMRF-EM framework applied to brain MR image segmentation.en
format.extent195124 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectFuzzy c-means clustering;en
subjectHidden markov random field model;en
subjectMagnetic resonance image;en
subjectMarkov random field model;en
subjectSegmentationen
titleUnsupervised Brain Magnetic Resonance Image Segmentation using HMRF-FCM frameworken
typeArticleen
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