Automatic segmentation of the left ventricle from dual contrast cardiac MR data

A. Pednekar, I. A. Kakadiaris, R. Muthupillai, S. Flamm

Research output: Contribution to journalConference articlepeer-review


Manual tracing of the blood pool from short axis cine MR images is routinely used to compute ejection fraction (EF) in clinical practice. The manual segmentation process is cumbersome, time consuming, and operator dependent. In this paper, we present an algorithm for the automatic computation of the EF that is based on segmenting the left ventricle by combining the fuzzy connectedness and deformable model frameworks. Our contributions are the following: 1) we automatically estimate a seed point and sample region for the fuzzy connectedness estimates, 2) we extend the fuzzy connectedness method to use adaptive weights for the homogeneity and the gradient energy functions that are computed dynamically, and 3) we extend the hybrid segmentation framework to allow forces from dual contrast and fuzzy connectedness data integrated with shape constraints. We compared our method against manual delineation performance by experienced radiologists on the data from nine asymptomatic volunteers with very encouraging results.

Original languageEnglish (US)
Pages (from-to)991-992
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - 2002
EventProceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States
Duration: Oct 23 2002Oct 26 2002


  • Cardiac MRI
  • Ejection fraction

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics


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