A geometric snake model for segmentation of medical imagery (1997)
| Venue: | IEEE Transactions on Medical Imaging |
| Citations: | 94 - 15 self |
BibTeX
@ARTICLE{Yezzi97ageometric,
author = {Anthony Yezzi and Satyanad Kichenassamy and Arun Kumar and Peter Olver and Allen Tannenbaum},
title = {A geometric snake model for segmentation of medical imagery},
journal = {IEEE Transactions on Medical Imaging},
year = {1997},
pages = {199--209}
}
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Abstract
Abstract — In this note, we employ the new geometric active contour models formulated in [25] and [26] for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. Thus, the snake is attracted very quickly and efficiently to the desired feature. Index Terms — Active contours, active vision, edge detection, gradient flows, segmentation, snakes. I.
