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LOCALLY WEIGHTED MARKOV RANDOM FIELDS FOR CORTICAL SEGMENTATION

Cardoso, MJ; Clarkson, MJ; Modat, M; Ridgway, GR; Ourselin, S; (2010) LOCALLY WEIGHTED MARKOV RANDOM FIELDS FOR CORTICAL SEGMENTATION. In: 2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO. (pp. 956 - 959). IEEE

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Abstract

Segmenting the human brain from magnetic resonance images is a challenging task due to the convoluted shape of the cortex, noise, intensity non-uniformity and partial volume effects. We propose a new way to overcome part of the bias-variance tradeoff existent in any segmentation technique by locally varying the behaviour of the model. We developed a novel metric based on the Laplacian of the geodesic distance to localise and iteratively modify the prior information and Markov random field weights, leading to a better delineation of deep sulci and narrow gyri. Experiments performed on 20 Brainweb datasets show statistically significant improvements in Dice scores and partial volume estimation when compared to two well established techniques.

Type: Proceedings paper
Title: LOCALLY WEIGHTED MARKOV RANDOM FIELDS FOR CORTICAL SEGMENTATION
Event: 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Location: Rotterdam, NETHERLANDS
Dates: 2010-04-14 - 2010-04-17
ISBN-13: 978-1-4244-4126-6
Keywords: Markov random field, Expectation-Maximisation, cortical segmentation, partial volume effect, MR-IMAGES, BRAIN, MODEL
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: http://discovery.ucl.ac.uk/id/eprint/101291
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