eprintid: 2777
rev_number: 7
eprint_status: archive
userid: 69
dir: disk0/00/00/27/77
datestamp: 2015-10-19 09:51:47
lastmod: 2016-04-05 12:19:09
status_changed: 2015-10-19 09:51:47
type: book_section
metadata_visibility: show
creators_name: Lara, Laura
creators_name: Vera, Sergio
creators_name: Perez, Frederic
creators_name: Lanconelli, Nico
creators_name: Morisi, Rita
creators_name: Donini, Bruno
creators_name: Turco, Dario
creators_name: Corsi, Cristiana
creators_name: Lamberti, Claudio
creators_name: Gavidia, Giovana
creators_name: Bordone, Maurizio
creators_name: Soudah, Eduardo
creators_name: Curzen, Nick
creators_name: Rosengarten, James
creators_name: Morgan, John
creators_name: Herrero, Javier
creators_name: González Ballester, Miguel A.
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creators_id: rita.morisi@imtlucca.it
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title: Supervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRI
ispublished: pub
subjects: QA75
subjects: T1
divisions: CSA
full_text_status: none
keywords: Myocardial Scar; Support Vector Machine; Level Set; Segmentation
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.
date: 2013
date_type: published
series: Lecture Notes in Computer Science
volume: 7746
publisher: Springer
pagerange: 53-61
pages: 9
id_number: 10.1007/978-3-642-36961-2_7
refereed: TRUE
isbn: 978-3-642-36960-5
book_title: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
official_url: http://dx.doi.org/10.1007/978-3-642-36961-2_7
citation:   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)