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Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information.

Zheng, Q; Lu, Z; Zhang, M; Xu, L; Ma, H; Song, S; Feng, Q; Feng, Y; Chen, W; He, T (2015) Automatic segmentation of myocardium from black-blood MR images using entropy and local neighborhood information. PLoS One, 10 (3). e0120018. ISSN 1932-6203 https://doi.org/10.1371/journal.pone.0120018
SGUL Authors: He, Taigang

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Abstract

By using entropy and local neighborhood information, we present in this study a robust adaptive Gaussian regularizing Chan-Vese (CV) model to segment the myocardium from magnetic resonance images with intensity inhomogeneity. By utilizing the circular Hough transformation (CHT) our model is able to detect epicardial and endocardial contours of the left ventricle (LV) as circles automatically, and the circles are used as the initialization. In the cost functional of our model, the interior and exterior energies are weighted by the entropy to improve the robustness of the evolving curve. Local neighborhood information is used to evolve the level set function to reduce the impact of the heterogeneity inside the regions and to improve the segmentation accuracy. An adaptive window is utilized to reduce the sensitivity to initialization. The Gaussian kernel is used to regularize the level set function, which can not only ensure the smoothness and stability of the level set function, but also eliminate the traditional Euclidean length term and re-initialization. Extensive validation of the proposed method on patient data demonstrates its superior performance over other state-of-the-art methods.

Item Type: Article
Additional Information: © 2015 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Keywords: Algorithms, Coronary Circulation, Heart Ventricles, Humans, Magnetic Resonance Imaging, Models, Theoretical, Myocardium, Myocardium, Heart Ventricles, Humans, Magnetic Resonance Imaging, Coronary Circulation, Algorithms, Models, Theoretical, General Science & Technology, MD Multidisciplinary
SGUL Research Institute / Research Centre: Academic Structure > Molecular and Clinical Sciences Research Institute (MCS)
Academic Structure > Molecular and Clinical Sciences Research Institute (MCS) > Cardiac (INCCCA)
Journal or Publication Title: PLoS One
ISSN: 1932-6203
Language: eng
Dates:
DateEvent
26 March 2015Published
26 January 2015Accepted
Publisher License: Creative Commons: Attribution 2.0
Projects:
Project IDFunderFunder ID
31000450National Science Foundation of ChinaUNSPECIFIED
2012J2200041Guangzhou Science FoundationUNSPECIFIED
2010CB732500Major State Basic Research Development Program of ChinaUNSPECIFIED
PubMed ID: 25811976
Web of Science ID: WOS:000356353700034
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/108753
Publisher's version: https://doi.org/10.1371/journal.pone.0120018

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