Flyer

Translational Biomedicine

  • ISSN: 2172-0479
  • Journal h-index: 12
  • Journal CiteScore: 8.06
  • Journal Impact Factor: 1.0
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
20+ Million Readerbase
Indexed In
  • Open J Gate
  • Genamics JournalSeek
  • JournalTOCs
  • ResearchBible
  • The Global Impact Factor (GIF)
  • China National Knowledge Infrastructure (CNKI)
  • CiteFactor
  • Scimago
  • Electronic Journals Library
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Proquest Summons
  • Publons
  • MIAR
  • University Grants Commission
  • Geneva Foundation for Medical Education and Research
  • Google Scholar
  • Secret Search Engine Labs
  • ResearchGate
Share This Page

Abstract

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

Ayush Goyal, Vinayak Ray

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.