Abstract

Delayed-enhancement magnetic resonance imaging (DE-MRI) is an effective technique for imaging left ventricular (LV) infarct. Existing techniques for LV infarct segmentation are primarily threshold-based making them prone to high user variability. In this work, we propose a segmentation algorithm that can learn from training images and segment based on this training model. This is implemented as a Markov random field (MRF) based energy formulation solved using graph-cuts. A good agreement was found with the Full-Width-at-Half-Maximum (FWHM) technique.
Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Subtitle of host publicationThird International Workshop, STACOM 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers
EditorsOscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young
PublisherSpringer Berlin Heidelberg
Pages71-79
Number of pages9
Volume7746 LNCS
ISBN (Electronic)978-3-642-36961-2
ISBN (Print)978-3-642-36960-5
DOIs
Publication statusPublished - 2013
Event3rd International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2012 - Nice, France
Duration: 5 Oct 20125 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7746 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference3rd International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2012
Country/TerritoryFrance
CityNice
Period5/10/20125/10/2012

Keywords

  • Delayed-enhancement MRI
  • Graph-cuts
  • Left ventricle
  • Segmentation

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