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Proceedings Paper

Training snakes to find object boundaries and evaluating them
Author(s): Samuel D. Fenster; John R. Kender
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Paper Abstract

We describe how to teach deformable models (snakes) to find object boundaries based on user-specified criteria, and we present a method for evaluating which criteria work best. These methods prove indispensable in abdominal CT images. Further work is needed in heart ultrasound images. The methods apply in any domain with consistent image conditions characterizing object boundaries, for which automated identification is nontrivial, perhaps due to interfering detail. A traditional strongest-edge-seeking snake fails to find an object's boundary when the strongest nearby image edges are not the ones sought. But we show how to instead learn, from training data, the relation between a shape and any image feature, as the probability distribution (PDF) of a function of image and shape. An important but neglected task has always been to select image qualities to guide a model. Because success depends on the relation of objective function (PDF) output to shape correctness, it is evaluated using a sampling of ground truth, a random model of the range of shapes tried during optimization, and a measure of shape closeness. The test results are evaluated for incidence of 'false positives' (scoring better than ground truth) versus incorrectness, and for the objective function's monotonicity with respect to incorrectness. Monotonicity is measured using correlation coefficient and using the newly introduced distance from closest increasing function. Domain-dependent choices must be tested. We analyze several Gaussian models fitting image intensity and perpendicular gradient at the object boundary, as well as the traditional sum of gradient magnitudes. The latter model is found inadequate in our domains; some of the former succeed.

Paper Details

Date Published: 17 August 2000
PDF: 14 pages
Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); doi: 10.1117/12.395569
Show Author Affiliations
Samuel D. Fenster, CUNY/City College (United States)
John R. Kender, Columbia Univ. (United States)

Published in SPIE Proceedings Vol. 4050:
Automatic Target Recognition X
Firooz A. Sadjadi, Editor(s)

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