@incollection{eprints2777,
       publisher = {Springer},
          author = {Laura Lara and Sergio Vera and Frederic Perez and Nico Lanconelli and Rita Morisi and Bruno Donini and Dario Turco and Cristiana Corsi and Claudio Lamberti and Giovana Gavidia and Maurizio Bordone and Eduardo Soudah and Nick Curzen and James Rosengarten and John Morgan and Javier Herrero and Miguel A. Gonz{\'a}lez Ballester},
       booktitle = {Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges},
          volume = {7746},
           pages = {53--61},
           title = {Supervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRI},
          series = {Lecture Notes in Computer Science},
            year = {2013},
             url = {http://eprints.imtlucca.it/2777/},
        abstract = {Delayed 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.},
        keywords = {Myocardial Scar; Support Vector Machine; Level Set; Segmentation}
}