Infarct segmentation of the left ventricle using graph-cuts

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Karim, Rashed, Chen, Zhong, Obom, Samantha, Ma, Ying-Liang ORCID: https://orcid.org/0000-0001-5770-5843, Acheampong, Prince, Gill, Harminder, Gill, Jaspal, Rinaldi, C. Aldo, O'Neill, Mark, Razavi, Reza, Schaeffter, Tobias and Rhode, Kawal S. (2013) Infarct segmentation of the left ventricle using graph-cuts. In: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Lecture Notes in Computer Science . Springer, Berlin, Heidelberg, 71–79. ISBN 978-3-642-36960-5

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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.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	04 Jan 2023 10:30
Last Modified:	04 Jan 2023 10:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/90378
DOI:	10.1007/978-3-642-36961-2_9

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