IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T09:12:36ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2015-10-19T09:51:47Z2016-04-05T12:19:09Zhttp://eprints.imtlucca.it/id/eprint/2777This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/27772015-10-19T09:51:47ZSupervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRIDelayed Enhancement Magnetic Resonance Imaging can be used to non-invasively differentiate viable from non-viable myocardium within the Left Ventricle in patients suffering from myocardial diseases. Automated segmentation of scarified tissue can be used to accurately quantify the percentage of myocardium affected. This paper presents a method for cardiac scar detection and segmentation based on supervised learning and level set segmentation. First, a model of the appearance of scar tissue is trained using a Support Vector Machines classifier on image-derived descriptors. Based on the areas detected by the classifier, an accurate segmentation is performed using a segmentation method based on level sets.Laura LaraSergio VeraFrederic PerezNico LanconelliRita Morisirita.morisi@imtlucca.itBruno DoniniDario TurcoCristiana CorsiClaudio LambertiGiovana GavidiaMaurizio BordoneEduardo SoudahNick CurzenJames RosengartenJohn MorganJavier HerreroMiguel A. González Ballester