{"version":1,"kind":"Article","sha256":"","slug":"612","location":"","dependencies":[],"doi":"10.54294/os009b","frontmatter":{"title":"MS Lesion Segmentation based on Hidden Markov Chains","abstract":"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.","license":"You are licensing your work to Kitware Inc. under the\nCreative Commons Attribution License Version 3.0.\n\nKitware Inc. agrees to the following:\n\nKitware is free\n * to copy, distribute, display, and perform the work\n * to make derivative works\n * to make commercial use of the work\n\nUnder the following conditions:\n\\\"by Attribution\\\" - Kitware must attribute the work in the manner specified by the author or licensor.\n\n * For any reuse or distribution, they must make clear to others the license terms of this work.\n * Any of these conditions can be waived if they get permission from the copyright holder.\n\nYour fair use and other rights are in no way affected by the above.\n\nThis is a human-readable summary of the Legal Code (the full license) available at\nhttp://creativecommons.org/licenses/by/3.0/legalcode","keywords":["Markov models","outlier detection","probabilistic atlas"],"authors":[{"name":"Bricq, Stephanie","email":"bricq@lsiit.u-strasbg.fr","affiliations":["LSIIT"],"corresponding":true},{"name":"Collet, Christophe","affiliations":[]},{"name":"Armspach, Jean-Paul","affiliations":[]}],"date_submitted":"2008-07-15T06:39:12Z","external_publication_id":612,"revision_cids":["bafkreic6flhhzcupajbm2odzbludhq4mgtbjzsod4wcexzjpueadtz3rtm"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreic4h46paqwfrsn6madzp52udiixslnohuu3aznfwbyynxtawbo4ji","title":"root/reviews.md","filename":"reviews.md","extra":{"size_bytes":1389,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreifanao7rwrjfjhlcx7f3ohavwbpfzuh3ja735k4f6zcu6wxfeaasm","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":8355,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreib2v5wmtf5rn2m67g2tz27773k5ep5gbypqku43a3tuolxapc3kz4","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":303296,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7","ref8","ref9","ref10","ref11","ref12"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.1007/11566465_51","html":"STREM: a robust multidimensional parametric method to segment MS lesions in MRI+MICCAI'+October 2005+3749+409+416+L.S.+P. 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