A Modified Image Segmentation Method Using Active Contour Model

DOI

10.2991/icecee-15.2015.218How to use a DOI?

Keywords

Active contours; Gradient vector flow; Laplace operator; Border leakage; External force field.

Abstract

Active contours, or snakes, have extensive applications in image segmentation. Conventional snakes have several drawbacks, such as the initialization contour sensitivity and border leakage phenomenon. Many new methods have been proposed to address these problems. In this paper, we present an improved image segmentation method based on snakes. Firstly, we adopt the multi-step direction method to enlarge the scope of initial contour and obtain more precise edge map. Then, we decompose the Laplace operator to tangential direction and normal direction, weakening the border smoothing effect. Finally, two correlational self-adaptive weight functions are added to the two directions. Thus, the snakes can adaptively adjust the weights of smoothing item and diffusion item through the local image characteristics. Based on the subjective and objective evaluations, the proposed method outperforms the state-of-the-art methods and improves the segmentation accuracy.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shiping Zhu
AU  - Ruidong Gao
PY  - 2015/06
DA  - 2015/06
TI  - A Modified Image Segmentation Method Using Active Contour Model
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 1162
EP  - 1168
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.218
DO  - 10.2991/icecee-15.2015.218
ID  - Zhu2015/06
ER  -