Journal of Digital Imaging

, Volume 24, Issue 4, pp 598–608 | Cite as

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


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.

Key words

Image segmentation cardiac imaging image analysis left ventricle 



We thank Sunnybrook Health Sciences Centre for making their clinical image data, ground truth contour data and evaluation software accessible to public.

We gratefully acknowledge funding for this research by the Biomedical Research Council, Agency for Science, Technology and Research, Singapore.


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Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • Su Huang
    • 1
  • Jimin Liu
    • 1
  • Looi Chow Lee
    • 1
  • Sudhakar K Venkatesh
    • 2
  • Lynette Li San Teo
    • 2
  • Christopher Au
    • 2
  • Wieslaw L. Nowinski
    • 1
  1. 1.Biomedical Imaging Lab, Singapore Bio-imaging ConsortiumAgency for Science, Technology and Research (A*STAR)SingaporeSingapore
  2. 2.Department of Diagnostic Radiology, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeSingapore

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