A geometric snake model for segmentation of medical imagery

Anthony Yezzi, Satyanad Kichenassamy, Arun Kumar, Peter Olver, Allen Tannenbaum

Research output: Contribution to journalArticlepeer-review

435 Scopus citations

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 languageEnglish (US)
Pages (from-to)199-209
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume16
Issue number2
DOIs
StatePublished - 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

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