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 |
|---|---|
| Pages (from-to) | 2556-2559 |
| Number of pages | 4 |
| Journal | IFMBE Proceedings |
| Volume | 14 |
| Issue number | 1 |
| Publication status | Published - 2007 Jan 1 |
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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 journal › Conference article
}
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
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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 -