Open Access

ARTICLE

Segmentation of the Left Ventricle in Cardiac MRI Using Random Walk Techniques

Osama S. Faragallah^1,*, Ghada Abdel-Aziz², Hala S. El-sayed³, Gamal G. N. Geweid^4,5

1 Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
2 Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
3 Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin El-Kom 32511, Egypt
4 Department of Electrical Engineering, Faculty of Engineering, Benha University, Benha 13512, Egypt
5 Department of Biomedical Engineering, College of Engineering & Computer Sciences, Marshall University, Huntington, WV 25755, USA

* Corresponding Author: Osama S. Faragallah. Email:

Intelligent Automation & Soft Computing 2021, 30(2), 575-588. https://doi.org/10.32604/iasc.2021.019023

Received 29 March 2021; Accepted 30 April 2021; Issue published 11 August 2021

Abstract

As a regular tool for assessing and diagnosing cardiovascular disease (CVD), medical professionals and health care centers, are highly dependent on cardiac imaging. The purpose of dividing the cardiac images is to paint the inner and outer walls of the heart to divide all or part of the limb’s boundaries. In order to enhance cardiologist in the process of cardiac segmentation, new and accurate methods are needed to divide the selected object, which is the left ventricle (LV). Segmentation techniques aim to provide a fast segmentation process and improve the reliability of the process. In this paper, a comparative study is made on basic random walk (BRW), extended random walk with priors (ERW), and high-speed random walk (HSRW) techniques. In the presented paper, we have applied three different types of medical image segmentation techniques to many Cardiovascular Magnetic Resonance images (CMRIs) in our experimental evaluation to lead statistically significant conclusion and confirm that our results are generalized. We have used 125 sets of CMRIs generated from five groups of patients with different types of cardiovascular disease to get the precise capacity of the productivity of the introduced method. In this paper, several performance metrics are used for instance correspondence coefficient D, distance, and PSNR. In the presented paper, a short-axis 3D multilayer CMRIs database has been taken and applied on many case studies to decide the outcomes of several segmentation methods. Throughout the experiments, the performance time for three segmentation methods are also calculated and utilized in the comparison process as another important performance factor. The experimental results show that ERW technique is the furthermost accurate segmentation technique among all the approaches.

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Keywords

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