Boundary detection via dynamic programming

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Abstract
This paper reports a new method for detecting optimal boundaries in multidimensional scene data via dynamic programming (DP). In its current form the algorithm detects 2-D contours on slices and differs from other reported DP-based algorithms in an essential way in that it allows freedom in 2-D for finding optimal contour paths (as opposed to a single degree of freedom in the published methods). The method is being successfully used in segmenting object boundaries in a variety of medical applications including orbital volume from CT images (for craniofacial surgical planning), segmenting bone in MR images for kinematic analysis of the joints of the foot, segmenting the surface of the brain from the inner surface of the cranial vault, segmenting pituitary gland tumor for following the effect of a drug on the tumor, segmenting the boundaries of the heart in MR images, and segmenting the olfactory bulb for verifying hypotheses related to the size of this bulb in certain disease states.

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