A convex max-flow segmentation of LV using subject-specific distributions on cardiac MRI

  • Authors:

  • Mohammad Saleh Nambakhsh;Jing Yuan;Ismail Ben Ayed;Kumaradevan Punithakumar;Aashish Goela;Ali Islam;Terry Peters;Shuo Li

  • Affiliations:

  • Biomedical Engineering Program, University of Western Ontario, London, Canada and Imaging Research Laboratories, Robarts Research Institute, London, ON, Canada;Computer Science Department, University of Western Ontario, London, Canada;GE Healthcare, London, ON, Canada;GE Healthcare, London, ON, Canada;Department of Medical Imaging, University of Western Ontario, London, Canada and St. Joseph's Health Care, London, ON, Canada;Department of Medical Imaging, University of Western Ontario, London, Canada and St. Joseph's Health Care, London, ON, Canada;Biomedical Engineering Program, University of Western Ontario, London, Canada and Imaging Research Laboratories, Robarts Research Institute, London, ON, Canada;Biomedical Engineering Program, University of Western Ontario, London, Canada and GE Healthcare, London, ON, Canada

  • Venue:
  • IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
  • Year:
  • 2011

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Abstract

This work studies the convex relaxation approach to the left ventricle (LV) segmentation which gives rise to a challenging multiregion seperation with the geometrical constraint. For each region, we consider the global Bhattacharyya metric prior to evaluate a gray-scale and a radial distance distribution matching. In this regard, the studied problem amounts to finding three regions that most closely match their respective input distribution model. It was previously addressed by curve evolution, which leads to sub-optimal and computationally intensive algorithms, or by graph cuts, which result in heavy metrication errors (grid bias). The proposed convex relaxation approach solves the LV segmentation through a sequence of convex sub-problems. Each sub-problem leads to a novel bound of the Bhattacharyya measure and yields the convex formulation which paves the way to build up the efficient and reliable solver. In this respect, we propose a novel flow configuration that accounts for labeling-function variations, in comparison to the existing flow-maximization configurations. We show it leads to a new convex max-flow formulation which is dual to the obtained convex relaxed sub-problem and does give the exact and global optimums to the original non-convex sub-problem. In addition, we present such flow perspective gives a new and simple way to encode the geometrical constraint of optimal regions. A comprehensive experimental evaluation on sufficient patient subjects demonstrates that our approach yields improvements in optimality and accuracy over related recent methods.