%I Springer
%V 7746
%P 53-61
%T Supervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRI
%R 10.1007/978-3-642-36961-2_7
%B Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
%D 2013
%L eprints2777
%X 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.
%S Lecture Notes in Computer Science
%K Myocardial Scar; Support Vector Machine; Level Set; Segmentation
%A Laura Lara
%A Sergio Vera
%A Frederic Perez
%A Nico Lanconelli
%A Rita Morisi
%A Bruno Donini
%A Dario Turco
%A Cristiana Corsi
%A Claudio Lamberti
%A Giovana Gavidia
%A Maurizio Bordone
%A Eduardo Soudah
%A Nick Curzen
%A James Rosengarten
%A John Morgan
%A Javier Herrero
%A Miguel A. Gonz?lez Ballester