A geometric snake model for segmentation of medical imagery

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

doi:10.1109/42.563665

A geometric snake model for segmentation of medical imagery

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

Mathematics

Research output: Contribution to journal › Article › peer-review

467 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 language	English (US)
Pages (from-to)	199-209
Number of pages	11
Journal	IEEE Transactions on Medical Imaging
Volume	16
Issue number	2
DOIs	https://doi.org/10.1109/42.563665
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

Access

10.1109/42.563665

OpenUrl availability

Full text

Cite this

@article{e0c5ea579460483387178d466e166f4b,

title = "A geometric snake model for segmentation of medical imagery",

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.",

keywords = "Active contours, Active vision, Edge detection, Gradient flows, Segmentation, Snakes",

author = "Anthony Yezzi and Satyanad Kichenassamy and Arun Kumar and Peter Olver and Allen Tannenbaum",

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.",

year = "1997",

doi = "10.1109/42.563665",

language = "English (US)",

volume = "16",

pages = "199--209",

journal = "IEEE Transactions on Medical Imaging",

issn = "0278-0062",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

TY - JOUR

T1 - A geometric snake model for segmentation of medical imagery

AU - Yezzi, Anthony

AU - Kichenassamy, Satyanad

AU - Kumar, Arun

AU - Olver, Peter

AU - Tannenbaum, Allen

N1 - 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.

PY - 1997

Y1 - 1997

N2 - 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.

AB - 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.

KW - Active contours

KW - Active vision

KW - Edge detection

KW - Gradient flows

KW - Segmentation

KW - Snakes

UR - http://www.scopus.com/inward/record.url?scp=0031106963&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031106963&partnerID=8YFLogxK

U2 - 10.1109/42.563665

DO - 10.1109/42.563665

M3 - Article

C2 - 9101329

AN - SCOPUS:0031106963

SN - 0278-0062

VL - 16

SP - 199

EP - 209

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 2

ER -

A geometric snake model for segmentation of medical imagery

Abstract

Bibliographical note

Keywords

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this