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.
Original language | English (US) |
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Pages (from-to) | 199-209 |
Number of pages | 11 |
Journal | IEEE Transactions on Medical Imaging |
Volume | 16 |
Issue number | 2 |
DOIs | |
State | Published - 1997 |
Bibliographical note
Funding Information:Manuscript received August 31, 1995; revised October 14, 1996. This work was supported in part by the National Science Foundation under Grant DMS-9204192 and Grant ECS-9122106, in part by the Air Force Office of Scientific Research under Grant F49620-94-1-0058DEF, and in part by the Army Research Office under Grant DAAH04-94-G-0054 and Grant DAAH04-93-G-0332. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was J. S. Duncan. Asterisk indicates corresponding author.
Keywords
- Active contours
- Active vision
- Edge detection
- Gradient flows
- Segmentation
- Snakes