A nonparametric statistical method for image segmentation using information theory and curve evolution

  • Authors:
  • Junmo Kim;J. W. Fisher, III;A. Yezzi;M. Cetin;A. S. Willsky

  • Affiliations:
  • Lab. for Inf. & Decision Syst., Massachusetts Inst. of Technol., Cambridge, MA, USA;-;-;-;-

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2005

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In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Furthermore, our method, which does not require any training, performs as good as methods based on training.