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; S. B. Cho; Y. H. Seol; J. S. Oh; J. M. Gu; J. H. Seol; J. S. Yu; Min Gi Kim; Kyung Sun

doi:10.1007/978-3-540-36841-0_644

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, S. B. Cho, Y. H. Seol, J. S. Oh, J. M. Gu, J. H. Seol, J. S. Yu, Min Gi Kim, Kyung Sun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English
Title of host publication	IFMBE Proceedings
Editors	Sun I. Kim, Tae Suk Suh
Publisher	Springer Verlag
Pages	2556-2559
Number of pages	4
Edition	1
ISBN (Print)	9783540368397
DOIs	https://doi.org/10.1007/978-3-540-36841-0_644
Publication status	Published - 2007
Event	10th World Congress on Medical Physics and Biomedical Engineering, WC 2006 - Seoul, Korea, Republic of Duration: 2006 Aug 27 → 2006 Sep 1

Publication series

Name	IFMBE Proceedings
Number	1
Volume	14
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Other

Other	10th World Congress on Medical Physics and Biomedical Engineering, WC 2006
Country/Territory	Korea, Republic of
City	Seoul
Period	06/8/27 → 06/9/1

Keywords

Active contour model
Morphological gradient
Segmentation
Snake

ASJC Scopus subject areas

Bioengineering
Biomedical Engineering

Access to Document

10.1007/978-3-540-36841-0_644

Cite this

Kim, H. C., Park, S. W., Cho, S. B., Seol, Y. H., Oh, J. S., Gu, J. M., Seol, J. H., Yu, J. S., Kim, M. G., & Sun, K. (2007). A study of medical image segmentation technique using active contour model based on morphological gradient: With some synthetic images. In S. I. Kim, & T. S. Suh (Eds.), IFMBE Proceedings (1 ed., pp. 2556-2559). (IFMBE Proceedings; Vol. 14, No. 1). Springer Verlag. https://doi.org/10.1007/978-3-540-36841-0_644

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, S. B. et al.

IFMBE Proceedings. ed. / Sun I. Kim; Tae Suk Suh. 1. ed. Springer Verlag, 2007. p. 2556-2559 (IFMBE Proceedings; Vol. 14, No. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. in SI Kim & TS Suh (eds), IFMBE Proceedings. 1 edn, IFMBE Proceedings, no. 1, vol. 14, Springer Verlag, pp. 2556-2559, 10th World Congress on Medical Physics and Biomedical Engineering, WC 2006, Seoul, Korea, Republic of, 06/8/27. https://doi.org/10.1007/978-3-540-36841-0_644

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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.",

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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.

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

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