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Proceedings Paper

Topologically correct cortical segmentation using Khalimsky's cubic complex framework
Author(s): Manuel Jorge Cardoso; Matthew J. Clarkson; Marc Modat; Hugues Talbot; Michel Couprie; Sébastien Ourselin
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Paper Abstract

Automatic segmentation of the cerebral cortex from magnetic resonance brain images is a valuable tool for neuroscience research. Due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cerebral cortex, segmenting the brain in a robust, accurate and topologically correct way still poses a challenge. In this paper we describe a topologically correct Expectation Maximisation based Maximum a Posteriori segmentation algorithm formulated within the Khalimsky cubic complex framework, where both the solution of the EM algorithm and the information derived from a geodesic distance function are used to locally modify the weighting of a Markov Random Field and drive the topology correction operations. Experiments performed on 20 Brainweb datasets show that the proposed method obtains a topologically correct segmentation without significant loss in accuracy when compared to two well established techniques.

Paper Details

Date Published: 11 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79620P (11 March 2011); doi: 10.1117/12.878190
Show Author Affiliations
Manuel Jorge Cardoso, Univ. College London (United Kingdom)
Matthew J. Clarkson, Univ. College London (United Kingdom)
Marc Modat, Univ. College London (United Kingdom)
Hugues Talbot, Univ. Paris-Est, CNRS, ESIEE (France)
Michel Couprie, Univ. Paris-Est, CNRS, ESIEE (France)
Sébastien Ourselin, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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