An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images

Su Huang, Jimin Liu, Looi Chow Lee, Sudhakar K. Venkatesh, Lynette Li San Teo, Christopher Au, Wieslaw L. Nowinski

Research output: Contribution to journalArticle

47 Scopus citations

Abstract

Segmentation of the left ventricle is important in the assessment of cardiac functional parameters. Manual segmentation of cardiac cine MR images for acquiring these parameters is time-consuming. Accuracy and automation are the two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach to segment the left ventricle from short axis cine cardiac MR images automatically. Our method incorporates a number of image processing and analysis techniques including thresholding, edge detection, mathematical morphology, and image filtering to build an efficient process flow. This process flow makes use of various features in cardiac MR images to achieve high accurate segmentation results. Our method was tested on 45 clinical short axis cine cardiac images and the results are compared with manual delineated ground truth (average perpendicular distance of contours near 2 mm and mean myocardium mass overlapping over 90%). This approach provides cardiac radiologists a practical method for an accurate segmentation of the left ventricle.

Original languageEnglish (US)
Pages (from-to)598-608
Number of pages11
JournalJournal of Digital Imaging
Volume24
Issue number4
DOIs
StatePublished - Aug 2011

Keywords

  • Cardiac imaging
  • Image analysis
  • Image segmentation
  • Left ventricle

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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