Work

Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model

Year: 2022

Type: conference-paper

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.

Source: Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...)

DOI: 10.11159/icsta22.146

Authors: Yalei Yang, Hao Gao, Colin Berry, Aleksandra Radjenovic, Dirk Husmeier

Institution: University of Glasgow

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: GlaxoSmithKline, Engineering and Physical Sciences Research Council