A Graph Based Methodology for Volumetric Left Ventricle Segmentation

  • S. P. Dakua
  • J. Abi Nahed
  • A. Al-Ansari
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 13)


Clinician-friendly methods for cardiac image segmentation in clinical practice remain a tough challenge. Despite increased image quality including medical imaging, image segmentation continues to represent a major bottleneck in practical applications due to noise and lack of contrast. Larger standard deviation in segmentation accuracy may be expected for automatic methods when the input dataset is varied; also at some instances the radiologists find them hard in case any correction is desired. In this context, this paper presents a semi-automatic algorithm that uses anisotropic diffusion for smoothing the image and enhancing the edges followed by a new graph cut method, AnnularCut, for 3D left ventricle segmentation from some pre-selected MR slices. The main contribution, in this work, is a new formulation for preventing the cellular automation method to leak into surrounding areas of similar intensity. Another contribution is the use of level sets for segmenting the slices automatically between the preselected slices segmented by the cellular automaton. Both qualitative and quantitative evaluation performed on York and MICCAI Grand Challenge workshop database of MR images reflect the potential of the proposed method.


Cellular automata Graph cut Segmentation MR 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Qatar Science and Technology Park \ QRSCQatar FoundationDehaQatar
  2. 2.Hamad Medical CorporationQatar FoundationDehaQatar

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