Title

Cardiovascular Segmentation Methods Based on Weak or no Prior

Author First name, Last name, Institution

Fatma Taher, Zayed University
Neema Prakash, Zayed University

Document Type

Conference Proceeding

Publication Date

12-1-2021

Abstract

In clinical diagnosis, segmentation of cardiac magnetic resonance imaging (MRI) plays a major role. For distinguishing inner and outer layer borders in the left and right ventricles of the human heart, automated segmentation methods are used instead of traditional diagnostic process which is very slow and requires more labors. This paper discusses some of the main segmentation methods such as image based, pixel classification and deformable models which are based on weak or no prior which means it does not require any prior knowledge to understand. Among these, multilevel thresholding, fully convolutional neural network (FCN) and active contour-based segmentation methods are mainly focused. These methods are able to successfully segment left ventricle (LV) and right ventricle (RV) and are able to achieve better performance in the classification of cardiac diseases.

ISBN

978-1-7281-8281-0

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

00

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Deformable models, Heart, Deep learning, Image segmentation, Thresholding (Imaging), Magnetic resonance imaging, Neural networks

Indexed in Scopus

no

Open Access

no

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