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Automated detection of white matter changes in elderly people using fuzzy, geostatistical, and information combining models
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia.ORCID iD: 0000-0002-4255-5130
Institute of Epidemiology and Social Medicine, University of Muenster, Germany.
2011 (English)In: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 15, no 2, 242-250 p.Article in journal (Refereed) Published
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Abstract [en]

Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can effectively address the issue of automated quantification of neuroimages, which are subject to different interests of various medical hypotheses. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The methods are based on the computational models of fuzzy clustering, possibilistic clustering, geostatistics, and knowledge combination. Experimental results on MRI data have shown that the proposed image analysis methodology can be applied as a very useful computerized tool for the validation of our particular medical question, where white matter changes of the brain are thought to be the most important social medical evidence.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2011. Vol. 15, no 2, 242-250 p.
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-127903DOI: 10.1109/TITB.2010.2081996ISI: 000288082200009PubMedID: 20889435Scopus ID: 2-s2.0-79952459806OAI: oai:DiVA.org:liu-127903DiVA: diva2:928796
Available from: 2016-05-16 Created: 2016-05-13 Last updated: 2017-06-29Bibliographically approved

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