MR brain image segmentation based on self-organizing map network

Li, Yan and Chi, Zheru (2005) MR brain image segmentation based on self-organizing map network. International Journal of Information Technology, 11 (8). pp. 45-53. ISSN 0218-7957


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.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Based on paper presented at 2005 International Conference on Intelligent Computing (ICIC 2005), 23-26 Aug 2005, Hefei, China. Permanent restricted access due to copyright policy of publisher (World Scientific)
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 30 Nov 2007 11:47
Last Modified: 03 Jul 2013 00:21
Uncontrolled Keywords: magnetic resonance imaging; self-organising feature maps; Markov random field; white matter; grey matter; cerebrospinal fluid
Fields of Research : 10 Technology > 1004 Medical Biotechnology > 100402 Medical Biotechnology Diagnostics (incl. Biosensors)
11 Medical and Health Sciences > 1103 Clinical Sciences > 110320 Radiology and Organ Imaging
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
?? 803 ??
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology

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