A study of medical image segmentation technique using active contour model based on morphological gradient: With some synthetic images

H. C. Kim, S. W. Park, Sung Bum Cho, Y. H. Seol, J. S. Oh, J. M. Gu, J. H. Seol, J. S. Yu, Min Gi Kim, Kyung Sun

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

Snake, also known as the Active Contour Model, is an actively developing research area for the image segmentation algorithm. Gradient Vector Flow (GVF) Snake resolved the problems associated with the initialization and concave region resulting from using the gradient vector flow as the external force. However, the problem resides in the use of Gaussian filtering that result in blurring effect on the object boundary. Consequently we have difficulties to find exact contour of the object boundary. In order to resolve this problem, the morphological gradient was used for a new edge map to create an external force more precise than that formed through the GVF Snake. For this experiment, we used three different types of synthetically generated images. All of the comparison tests were carried out under the same conditions (i.e. with same parameters in the GVF Snake algorithm) that result in the GVF Snake made the optimal movement. Even though, the improvements of our algorithm are clearly observed in the results. We evaluated the results with estimation error and minimum distance error.

Original languageEnglish
Pages (from-to)2556-2559
Number of pages4
JournalIFMBE Proceedings
Volume14
Issue number1
Publication statusPublished - 2007 Jan 1

Fingerprint

Image segmentation
Error analysis
Experiments

Keywords

  • Active contour model
  • Morphological gradient
  • Segmentation
  • Snake

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

Cite this

A study of medical image segmentation technique using active contour model based on morphological gradient : With some synthetic images. / Kim, H. C.; Park, S. W.; Cho, Sung Bum; Seol, Y. H.; Oh, J. S.; Gu, J. M.; Seol, J. H.; Yu, J. S.; Kim, Min Gi; Sun, Kyung.

In: IFMBE Proceedings, Vol. 14, No. 1, 01.01.2007, p. 2556-2559.

Research output: Contribution to journalConference article

Kim, HC, Park, SW, Cho, SB, Seol, YH, Oh, JS, Gu, JM, Seol, JH, Yu, JS, Kim, MG & Sun, K 2007, 'A study of medical image segmentation technique using active contour model based on morphological gradient: With some synthetic images', IFMBE Proceedings, vol. 14, no. 1, pp. 2556-2559.
Kim, H. C. ; Park, S. W. ; Cho, Sung Bum ; Seol, Y. H. ; Oh, J. S. ; Gu, J. M. ; Seol, J. H. ; Yu, J. S. ; Kim, Min Gi ; Sun, Kyung. / A study of medical image segmentation technique using active contour model based on morphological gradient : With some synthetic images. In: IFMBE Proceedings. 2007 ; Vol. 14, No. 1. pp. 2556-2559.
@article{6a330ae9f5cb4fbf8a7a00374143cb81,
title = "A study of medical image segmentation technique using active contour model based on morphological gradient: With some synthetic images",
abstract = "Snake, also known as the Active Contour Model, is an actively developing research area for the image segmentation algorithm. Gradient Vector Flow (GVF) Snake resolved the problems associated with the initialization and concave region resulting from using the gradient vector flow as the external force. However, the problem resides in the use of Gaussian filtering that result in blurring effect on the object boundary. Consequently we have difficulties to find exact contour of the object boundary. In order to resolve this problem, the morphological gradient was used for a new edge map to create an external force more precise than that formed through the GVF Snake. For this experiment, we used three different types of synthetically generated images. All of the comparison tests were carried out under the same conditions (i.e. with same parameters in the GVF Snake algorithm) that result in the GVF Snake made the optimal movement. Even though, the improvements of our algorithm are clearly observed in the results. We evaluated the results with estimation error and minimum distance error.",
keywords = "Active contour model, Morphological gradient, Segmentation, Snake",
author = "Kim, {H. C.} and Park, {S. W.} and Cho, {Sung Bum} and Seol, {Y. H.} and Oh, {J. S.} and Gu, {J. M.} and Seol, {J. H.} and Yu, {J. S.} and Kim, {Min Gi} and Kyung Sun",
year = "2007",
month = "1",
day = "1",
language = "English",
volume = "14",
pages = "2556--2559",
journal = "IFMBE Proceedings",
issn = "1680-0737",
publisher = "Springer Verlag",
number = "1",

}

TY - JOUR

T1 - A study of medical image segmentation technique using active contour model based on morphological gradient

T2 - With some synthetic images

AU - Kim, H. C.

AU - Park, S. W.

AU - Cho, Sung Bum

AU - Seol, Y. H.

AU - Oh, J. S.

AU - Gu, J. M.

AU - Seol, J. H.

AU - Yu, J. S.

AU - Kim, Min Gi

AU - Sun, Kyung

PY - 2007/1/1

Y1 - 2007/1/1

N2 - Snake, also known as the Active Contour Model, is an actively developing research area for the image segmentation algorithm. Gradient Vector Flow (GVF) Snake resolved the problems associated with the initialization and concave region resulting from using the gradient vector flow as the external force. However, the problem resides in the use of Gaussian filtering that result in blurring effect on the object boundary. Consequently we have difficulties to find exact contour of the object boundary. In order to resolve this problem, the morphological gradient was used for a new edge map to create an external force more precise than that formed through the GVF Snake. For this experiment, we used three different types of synthetically generated images. All of the comparison tests were carried out under the same conditions (i.e. with same parameters in the GVF Snake algorithm) that result in the GVF Snake made the optimal movement. Even though, the improvements of our algorithm are clearly observed in the results. We evaluated the results with estimation error and minimum distance error.

AB - Snake, also known as the Active Contour Model, is an actively developing research area for the image segmentation algorithm. Gradient Vector Flow (GVF) Snake resolved the problems associated with the initialization and concave region resulting from using the gradient vector flow as the external force. However, the problem resides in the use of Gaussian filtering that result in blurring effect on the object boundary. Consequently we have difficulties to find exact contour of the object boundary. In order to resolve this problem, the morphological gradient was used for a new edge map to create an external force more precise than that formed through the GVF Snake. For this experiment, we used three different types of synthetically generated images. All of the comparison tests were carried out under the same conditions (i.e. with same parameters in the GVF Snake algorithm) that result in the GVF Snake made the optimal movement. Even though, the improvements of our algorithm are clearly observed in the results. We evaluated the results with estimation error and minimum distance error.

KW - Active contour model

KW - Morphological gradient

KW - Segmentation

KW - Snake

UR - http://www.scopus.com/inward/record.url?scp=79961195798&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79961195798&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:79961195798

VL - 14

SP - 2556

EP - 2559

JO - IFMBE Proceedings

JF - IFMBE Proceedings

SN - 1680-0737

IS - 1

ER -