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Maximum a posteriori estimation of image boundaries by dynamic programming

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  • C. A. Glasbey
  • M. J. Young

Abstract

Summary. We seek a computationally fast method for solving a difficult image segmentation problem: the positioning of boundaries on medical scanner images to delineate tissues of interest. We formulate a Bayesian model for image boundaries such that the maximum a posterioriestimator is obtainable very efficiently by dynamic programming. The prior model for the boundary is a biased random walk and the likelihood is based on a border appearance model, with parameter values obtained from training images. The method is applied successfully to the segmentation of ultrasound images and X‐ray computed tomographs of sheep, for application in sheep breeding programmes.

Suggested Citation

  • C. A. Glasbey & M. J. Young, 2002. "Maximum a posteriori estimation of image boundaries by dynamic programming," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(2), pages 209-221, May.
    • Handle: RePEc:bla:jorssc:v:51:y:2002:i:2:p:209-221
    DOI: 10.1111/1467-9876.00264
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