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Sectored snakes: evaluating learned-energy segmentations

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2 Author(s)
Fenster, S.D. ; Dept. of Comput. Sci., City Coll. of New York, NY, USA ; Kender, J.R.

We describe how to teach deformable models to maximize image segmentation correctness based on user-specified criteria, and present a method for evaluating which criteria work best. We show how to evaluate the efficacy of any resulting deformable model, given a sampling of ground truth, a model of the range of shapes tried during optimization, and a measure of shape closeness. In the domain of abdominal CT images, we demonstrate such evaluation on a simple “sectoring” of a snake in which intensity and perpendicular gradient are observed over equal-length segments. This specific set of qualities shows a measured improvement over an objective function that is uniform around the shape, and it follows naturally from examination of the latter's failures due to image variations around the organ boundary

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:23 ,  Issue: 9 )

Date of Publication:

Sep 2001

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