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MS Lesion Segmentation based on Hidden Markov Chains

Armspach, Jean-Paul, Bricq, Stephanie, Collet, Christophe
LSIIT
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1450
Published in The MIDAS Journal - MICCAI 2008 Workshop: MS Lesion Segmentation.
Submitted by Stephanie Bricq on 2008-07-15T06:39:12Z.

In this paper, we present a new automatic robust algorithm to segment multimodal brain MR images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using a Hidden Markov Chain (HMC) model and detects MS lesions as outliers to the model. For this aim, we use the Trimmed Likelihood Estimator (TLE) to extract outliers. Furthermore, neighborhood information is included using the HMC model and we propose to incorporate a priori information brought by a probabilistic atlas.