Automatic graph cut segmentation of multiple sclerosis lesionsReport as inadecuate




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* Corresponding author 1 VisAGeS - Vision, Action et Gestion d-informations en Santé INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE

Abstract : A fully automated segmentation algorithm for Multiple Sclerosis MS lesions is presented.
Our method includes two main steps: the detection of lesions by graph cut initialized with a robust Expectation-Maximization EM algorithm and the application of rules to remove false positives.
Our algorithm will be tested on the ISBI 2015 challenge longitudinal data.
For each patient, a unique parameter set is used to run the algorithm.


Keywords : Graph Cut Expectation-Maximization multiple sclerosis tissue classification





Author: Laurence Catanese - Olivier Commowick - Christian Barillot -

Source: https://hal.archives-ouvertes.fr/



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