TY  - CHAP
ID  - eprints2777
T2  - Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
EP  - 61
KW  - Myocardial Scar; Support Vector Machine; Level Set; Segmentation
AV  - none
TI  - Supervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRI
UR  - http://dx.doi.org/10.1007/978-3-642-36961-2_7
SN  - 978-3-642-36960-5
M1  - 7746
N2  - 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.
Y1  - 2013///
PB  - Springer
A1  - Lara, Laura
A1  - Vera, Sergio
A1  - Perez, Frederic
A1  - Lanconelli, Nico
A1  - Morisi, Rita
A1  - Donini, Bruno
A1  - Turco, Dario
A1  - Corsi, Cristiana
A1  - Lamberti, Claudio
A1  - Gavidia, Giovana
A1  - Bordone, Maurizio
A1  - Soudah, Eduardo
A1  - Curzen, Nick
A1  - Rosengarten, James
A1  - Morgan, John
A1  - Herrero, Javier
A1  - González Ballester, Miguel A.
SP  - 53
T3  - Lecture Notes in Computer Science
ER  -