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

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

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.

Citation Download Citation

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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