alexa Abstract | Belongingness Clustering and Region Labeling Based Pixel Classification for Automatic Left Ventricle Segmentation in Cardiac MRI Images

Translational Biomedicine
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Research Article Open Access

Abstract

This paper presents a fully automatic rapid method for delineation of the left ventricle (LV) from MRI images of heart patients for the critical diagnosis of myocardial function as an evaluation of heart disease. In this research, completely automated image segmentation is performed using a belongingness clustering and region labeling based pixel classification approach. This new combined region labeling and belongingness clustering technique removes the need for manual initialization, which is required in deformable methods. The left ventricle is segmented automatically in all slices in the multi-frame MRI data of the whole cardiac cycle rapidly in 0.67 seconds for a single frame on average. Manual segmentation of the left ventricle in the multi-frame cardiac MRI image data by experts was used as a standard to test the accuracy of the automated left ventricle segmentation method. Medical parameters like End Systolic Volume (ESV), End Diastolic Volume (EDV) and Ejection Fraction (EF) were calculated both automatically and manually and compared for accuracy.

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Author(s): Ayush Goyal Vinayak Ray

Keywords

End Systolic Volume (ESV), End Diastolic Volume (EDV), Ejection Fraction (EF), Molecular Development Biology,Translational Research and Clinical Intervention

 
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