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Maximum a posteriori estimation of image boundaries by dynamic programming
Author(s) -
Glasbey C. A.,
Young M. J.
Publication year - 2002
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00264
Subject(s) - maximum a posteriori estimation , dynamic programming , segmentation , computer science , artificial intelligence , image segmentation , computer vision , image (mathematics) , boundary (topology) , maximum likelihood , a priori and a posteriori , bayesian probability , scanner , mathematics , algorithm , statistics , epistemology , mathematical analysis , philosophy
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 posteriori estimator 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.