Lara, Laura and Vera, Sergio and Perez, Frederic and Lanconelli, Nico and Morisi, Rita and Donini, Bruno and Turco, Dario and Corsi, Cristiana and Lamberti, Claudio and Gavidia, Giovana and Bordone, Maurizio and Soudah, Eduardo and Curzen, Nick and Rosengarten, James and Morgan, John and Herrero, Javier and González Ballester, Miguel A. Supervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRI. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Lecture Notes in Computer Science, 7746 . Springer, pp. 53-61. ISBN 978-3-642-36960-5 (2013)
Full text not available from this repository.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.
| Item Type: | Book Section |
|---|---|
| Identification Number: | 10.1007/978-3-642-36961-2_7 |
| Uncontrolled Keywords: | Myocardial Scar; Support Vector Machine; Level Set; Segmentation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Research Area: | Computer Science and Applications |
| Depositing User: | Caterina Tangheroni |
| Date Deposited: | 19 Oct 2015 09:51 |
| Last Modified: | 05 Apr 2016 12:19 |
| URI: | http://eprints.imtlucca.it/id/eprint/2777 |
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