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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Infarct Segmentation of the Left Ventricle Using Graph-Cuts
verfasst von : Rashed Karim, Zhong Chen, Samantha Obom, Ying-Liang Ma, Prince Acheampong, Harminder Gill, Jaspal Gill, C. Aldo Rinaldi, Mark O’Neill, Reza Razavi, Tobias Schaeffter, Kawal S. Rhode
Erschienen in: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Verlag: Springer Berlin Heidelberg
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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.