Left Ventricle of Cardiovascular Image Segmentation using T-SegNet Hybrid and Extended Buffalo Optimization

Abstract

Today, cardiac disease is one of the most promising cause of mortality. The segmentation of the cardiac
image is an essential process to generate personalized models of the heart and to quantify the parameters
of cardiac performance. One of the important step is to perform segmentation in Left Ventricle (LR) of
cardiac using magnetic resonance images (MRI).However, which can used to find important parameters
to be mentioned stroke volume, discharge section, the structure of the left ventricle myocardium. In
addition, the segmentation of the left ventricle helps to build personalized cardiac computer models in
order to perform digital simulations. Right now, it is observed that no automated segmentation methods
related to cardiac images derived accurate performance. In this article, a new hybrid architectures is
proposed where T-Net architecture can combined withSeg-Netto reduced network parameters and used
for classification of cardiac MRI images .Then, in order to retrieve an approving performance, we use the
EBO (Extended Buffalo Optimization) algorithm to solve the cardiac segmentation. Experimental results
show that the proposed method successfully segments LR and achieves 90% accuracy in the cardiac
images.