In this note we employ the new geometric active contour models formulated in  and  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.
Bibliographical noteFunding 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.
- Active contours
- Active vision
- Edge detection
- Gradient flows