Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29112
Title: Mr brain image segmentation based on self-organizing map network
Authors: Li, Y
Chi, Z 
Keywords: Magnetic resonance imaging
Self-organising feature maps
Markov random field
White matter
Grey matter
Cerebrospinal fluid
Issue Date: 2005
Publisher: World Scientific
Source: International journal of information technology, 2005, v. 11, no. 8, p. 45-53 How to cite?
Journal: International journal of information technology 
Abstract: Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. The goal of magnetic resonance (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. A new unsupervised MR image segmentation method based on self-organizing feature map (SOFM) network is presented. The algorithm includes spatial constraints by using a Markov Random Field (MRF) model. The MRF term introduces the prior distribution with clique potentials and thus improves the segmentation results without having extra data samples in the training set or a complicated network structure. The simulation results demonstrate that the proposed algorithm is promising.
URI: http://hdl.handle.net/10397/29112
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