Topologically correct cortical segmentation using Khalimsky's cubic complex framework

Manuel Jorge Cardoso; Matthew J. Clarkson; Marc Modat; Hugues Talbot; Michel Couprie; Sébastien Ourselin

doi:10.1117/12.878190

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11 March 2011 Topologically correct cortical segmentation using Khalimsky's cubic complex framework

Manuel Jorge Cardoso, Matthew J. Clarkson, Marc Modat, Hugues Talbot, Michel Couprie, Sébastien Ourselin

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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79620P (2011) https://doi.org/10.1117/12.878190
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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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Manuel Jorge Cardoso, Matthew J. Clarkson, Marc Modat, Hugues Talbot, Michel Couprie, and Sébastien Ourselin "Topologically correct cortical segmentation using Khalimsky's cubic complex framework", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79620P (11 March 2011); https://doi.org/10.1117/12.878190

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