Image Segmentation Using Binary Level Set Method Based on Region-Based GAC Model

Ge Ren; Xing Qin Cao; Wei Min Pan; Yong Yang

doi:10.4028/www.scientific.net/KEM.480-481.1206

Paper Titles

Reconfiguration of Shipboard Power System Using Improved Ant Colony Optimization
p.1185

The Optimization of Automated Stereoscopic Warehouse’s Cargo Space Allocation Based on Hybrid Particle Swarm Algorithm
p.1191

Research on Project Cost Management Model of Ecological Construction
p.1197

Design and Implement of a Food Safety Testing Instrument Based on Microcontroller
p.1201

Image Segmentation Using Binary Level Set Method Based on Region-Based GAC Model
p.1206

Sensitivity Analysis of Cost Parameters of Sequential Imperfect Preventive Maintenance Model and Applications
p.1210

A Constructive Genetic Algorithm for the P- Median Location Problem of Typhoon Emergency Shelter in China Coastal Rural Areas
p.1215

Research on the Application of Green Technology to Leather Shoes in their Life Cycle
p.1221

The Influence Factor Analysis for Coupling Performance of HSK Tool System
p.1225

HomeKey Engineering MaterialsKey Engineering Materials Vols. 480-481Image Segmentation Using Binary Level Set Method...

Image Segmentation Using Binary Level Set Method Based on Region-Based GAC Model

Abstract:

A new Region-based GAC (geodesic active contour) model was presented, which is the improvement of traditional GAC model. A new region-based signed pressure forces function was presented, which takes the place of the edge stopping function, and can efficiently solve the problem of segmentation of objects with weak edges or without edges. The model is implemented by level set method with a binary level set function to reduce the expensive computational cost of re-initialization of the traditional level set function. The proposed algorithm has been applied to images of different modalities with promising results, which are better than that of traditional GAC model and C-V model.

You might also be interested in these eBooks

Materials Engineering for Advanced Technologies

View Preview

Info:

Periodical:

Key Engineering Materials (Volumes 480-481)

Pages:

1206-1209

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.480-481.1206

Citation:

Cite this paper

Online since:

June 2011

Authors:

Ge Ren, Xing Qin Cao, Wei Min Pan, Yong Yang

Keywords:

Active Contour, Image Segmentation, Level Set, Re-Initialization

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Kass M, Witkin A, TerzopoulosD. Snakes: Active Contour Models[C]/Proceeding of the 1st International Conference on Computer Vision. 1987, 1(1): 259-268.

Google Scholar

[2] S. Osher,J. A. Sethian. Fronts Propagating with Curvature Dependent Speed: Algorithm Based Hamilton-JacobiFormulation[J].J. Comp. Phys. 1988, 79(1): 12-49.

DOI: 10.1016/0021-9991(88)90002-2

Google Scholar

[3] D. Cremers, M. Rousson, R. Deriche. A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape, Int'l J. Comp. Vis. 2007, 72 (2): 195–215.

DOI: 10.1007/s11263-006-8711-1

Google Scholar

[4] J. Piovano, M. Rousson, T. Papadopoulo. Efficient segmentation of piecewise smooth images, SSVM07, Ischia, Italy, 2007, pp: 709–720.

DOI: 10.1007/978-3-540-72823-8_61

Google Scholar

[5] C. Li and C. Kao and J. Gore, et al. Minimization of Region-Scalable Fitting Energy for Image Segmentation[J]. IEEE Trans. Imag. Proc, 2008, 17: 1940-(1949).

DOI: 10.1109/tip.2008.2002304

Google Scholar

[6] L. Wang, C. Li, Q. Sun, D. Xia, C. Kao, Brain MR image segmentation using local and global intensity ﬁtting active contours/surfaces, Proceedings of Medical Image Computing and Computer Aided Intervention (MICCAI), Vol. LNCS 5241, Part I, 2008, pp: 384–392.

DOI: 10.1007/978-3-540-85988-8_46

Google Scholar