Topologically correct cortical segmentation using Khalimsky's cubic complex framework

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.
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Conference papers
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https://hal-upec-upem.archives-ouvertes.fr/hal-00728914
Contributor : Michel Couprie <>
Submitted on : Friday, September 7, 2012 - 8:18:30 AM
Last modification on : Thursday, July 5, 2018 - 2:29:10 PM

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Jorge Cardoso, Matthew J. Clarkson, Marc Modat, Hugues Talbot, Michel Couprie, et al.. Topologically correct cortical segmentation using Khalimsky's cubic complex framework. Medical Imaging 2011.: Image Processing, SPIE, Feb 2011, Lake Buena Vista, FL, United States. pp.1-8, ⟨10.1117/12.878190⟩. ⟨hal-00728914⟩

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