Open AccessA robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation
Author(s) -
Daniel García-Lorenzo,
Sylvain Prima,
S. P. Morrissey,
Christian Barillot
Publication year - 2008
Language(s) - English
DOI - 10.54294/1pbpd4
Subject(s) - multiple sclerosis , workflow , segmentation , computer science , artificial intelligence , pattern recognition (psychology) , lesion , maximization , expectation–maximization algorithm , computer vision , medicine , mathematics , mathematical optimization , maximum likelihood , pathology , statistics , database , psychiatry
A fully automatic workflow for Multiple Sclerosis (MS) lesion segmentation is described. Fully automatic means that no user interaction is performed in any of the steps and that all parameters are fixed for all the images processed in beforehand. Our workflow is composed of three steps: an intensity inhomogeneity (IIH) correction, skull-stripping and MS lesions segmentation. A validation comparing our results with two experts is done on MS MRI datasets of 24 MS patients from two different sites.