Yang, Y., Gao, H. , Berry, C. , Radjenovic, A. and Husmeier, D. (2022) Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model. In: Proceedings of the 4th International Conference on Statistics: Theory and Applications (ICSTA 22), Prague, Czech Republic, 28-30 July 2022, ISBN 9781990800085 (doi: 10.11159/icsta22.146)
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Publisher's URL: https://avestia.com/ICSTA2022_Proceedings/files/paper/ICSTA_146.pdf
Abstract
Dynamic Contract Enhanced Magnetic Resonance (MR) Imaging (DCE-MRI) has been widely used as a non-invasive assessment approach to estimate the myocardial blood flow (MBF). The delineation of a hypo-perfused region (low MBF region) is important for understanding a patient’s heart condition in clinical diagnosis. In this paper, a Markov random field constrained Gaussian mixture model (GMM-MRF) classification method is introduced to classify MBF maps using myocardial perfusion DCE-MRI data. The GMM-MRF method, trained with an ICM algorithm, makes use of spatial neighbourhood information to improve classification accuracy. The proposed method is applied to and assessed on both synthetic and clinical data, and compared with established classification methods.
Item Type: | Conference Proceedings |
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Additional Information: | This work was funded by EPSRC, grant reference numbers EP/T017899/1 and EP/S020950/1. Yalei Yang is funded by a grant from GlaxoSmithKline plc. |
Keywords: | DCE-MRI, myocardial perfusion, classification, lesion delineation, Gaussian Mixture Model, Markov random field constrained Gaussian mixture model. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Berry, Professor Colin and Gao, Dr Hao and Yang, Dr Yalei and Husmeier, Professor Dirk and Radjenovic, Dr Aleksandra |
Authors: | Yang, Y., Gao, H., Berry, C., Radjenovic, A., and Husmeier, D. |
Subjects: | Q Science > QA Mathematics |
College/School: | College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health College of Science and Engineering > School of Mathematics and Statistics > Mathematics College of Science and Engineering > School of Mathematics and Statistics > Statistics |
ISSN: | 2562-7767 |
ISBN: | 9781990800085 |
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