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. © 2013 Springer-Verlag.
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Karim, R., Chen, Z., Obom, S., Ma, Y. L., Acheampong, P., Gill, H., … Rhode, K. S. (2013). Infarct segmentation of the left ventricle using graph-cuts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7746 LNCS, pp. 71–79). https://doi.org/10.1007/978-3-642-36961-2_9
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