Segmentation of sub-cortical structures by the graph-shifts algorithm

  • Authors:

  • Jason J. Corso;Zhuowen Tu;Alan Yuille;Arthur Toga

  • Affiliations:

  • Center for Computational Biology, Laboratory of Neuro Imaging, University of California, Los Angeles;Center for Computational Biology, Laboratory of Neuro Imaging, University of California, Los Angeles;Department of Statistics, University of California, Los Angeles;Center for Computational Biology, Laboratory of Neuro Imaging, University of California, Los Angeles

  • Venue:
  • IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
  • Year:
  • 2007

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Abstract

We propose a novel algorithm called graph-shifts for performing image segmentation and labeling. This algorithm makes use of a dynamic hierarchical representation of the image. This representation allows each iteration of the algorithm to make both small and large changes in the segmentation, similar to PDE and split-and-merge methods, respectively. In particular, at each iteration we are able to rapidly compute and select the optimal change to be performed. We apply graph-shifts to the task of segmenting sub-cortical brain structures. First we formalize this task as energy function minimization where the energy terms are learned from a training set of labeled images. Then we apply the graphshifts algorithm. We show that the labeling results are comparable in quantitative accuracy to other approaches but are obtained considerably faster: by orders of magnitude (roughly one minute). We also quantitatively demonstrate robustness to initialization and avoidance of local minima in which conventional boundary PDE methods fall.