Adaptive active contour algorithms for extracting and mapping thick curves

Chris Davatzikos, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Thick curves arise naturally in certain applications such as magnetic resonance imaging of the brain; they can also arise in computer vision problems through morphological dilation of boundaries of objects. In this paper we describe two new adaptive active contour algorithms for the extraction and mapping of the skeleton of a thick curve. They are based on conditions that have been derived in previous work which guarantee uniqueness and fidelity of the solution. Both algorithms modify the regularization constant Ko in attempt to maintain convexity of the energy function while simultaneously improving the fidelity of the result. The first algorithm changes Ko over time while the second adapts Ko spatially. We evaluate both algorithms on experiments with synthetic curves; both demonstrate an improved performance compared to a fixed parameter active contour algorithm.

Original languageEnglish (US)
Title of host publicationIEEE Computer Vision and Pattern Recognition
Editors Anon
PublisherPubl by IEEE
Pages524-528
Number of pages5
ISBN (Print)0818638826
StatePublished - Dec 1 1993
EventProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
Duration: Jun 15 1993Jun 18 1993

Publication series

NameIEEE Computer Vision and Pattern Recognition

Other

OtherProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CityNew York, NY, USA
Period6/15/936/18/93

ASJC Scopus subject areas

  • Engineering(all)

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