Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata
Contributor : Michel Couprie Connect in order to contact the contributor
Submitted on : Friday, September 7, 2012 - 8:18:30 AM
Last modification on : Saturday, January 15, 2022 - 3:58:21 AM



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⟩



Record views