Please use this identifier to cite or link to this item: https://doi.org/10.1109/TBME.2013.2237907
Title: A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images
Authors: Wei, D.
Sun, Y. 
Ong, S.-H. 
Chai, P.
Teo, L.L.
Low, A.F.
Keywords: Cardiac MRI
classification
infarction quantification
late gadolinium enhanced (LGE)
segmentation
Issue Date: 2013
Source: Wei, D., Sun, Y., Ong, S.-H., Chai, P., Teo, L.L., Low, A.F. (2013). A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images. IEEE Transactions on Biomedical Engineering 60 (6) : 1499-1508. ScholarBank@NUS Repository. https://doi.org/10.1109/TBME.2013.2237907
Abstract: Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly visualize nonviable myocardium with hyperenhanced intensities with respect to normal myocardium. For heart attack patients, it is crucial to facilitate the decision of appropriate therapy by analyzing and quantifying their LGE CMR images. To achieve accurate quantification, LGE CMR images need to be processed in two steps: segmentation of the myocardium followed by classification of infarcts within the segmented myocardium. However, automatic segmentation is difficult usually due to the intensity heterogeneity of the myocardium and intensity similarity between the infarcts and blood pool. Besides, the slices of an LGE CMR dataset often suffer from spatial and intensity distortions, causing further difficulties in segmentation and classification. In this paper, we present a comprehensive 3-D framework for automatic quantification of LGE CMR images. In this framework, myocardium is segmented with a novel method that deforms coupled endocardial and epicardial meshes and combines information in both short-and long-axis slices, while infarcts are classified with a graph-cut algorithm incorporating intensity and spatial information. Moreover, both spatial and intensity distortions are effectively corrected with specially designed countermeasures. Experiments with 20 sets of real patient data show visually good segmentation and classification results that are quantitatively in strong agreement with those manually obtained by experts. © 1964-2012 IEEE.
Source Title: IEEE Transactions on Biomedical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/81853
ISSN: 00189294
DOI: 10.1109/TBME.2013.2237907
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