Normalized Gradient Vector Diffusion and Image Segmentation
In this paper, we present an approach for image segmentation, based on the existing Active Snake Model and Watershed-based Region Merging. Our algorithm includes initial segmentation using Normalized Gradient Vector Diffusion (NGVD) and region merging based on Region Adjacency Graph (RAG). We use a set of heat diffusion equations to generate a vector field over the image domain, which provides us with a natural way to define seeds as well as an external force to attract the active snakes. Then an initial segmentation of the original image can be obtained by a similar idea as seen in active snake model. Finally an RAG-based region merging technique is used to find the true segmentation as desired. The experimental results show that our NGVD-based region merging algorithm overcomes some problems as seen in classic active snake model. We will also see that our NGVD has several advantages over the traditional gradient vector diffusion.
KeywordsImage segmentation Gradient vector diffusion Heat diffusion equation Active snake model Watershed method Region merging
Unable to display preview. Download preview PDF.
- 4.Xu, C., Prince, J.L.: Gradient Vector Flow Deformable Models. Handbook of Medical Imaging, edited by Isaac Bankman, Academic Press. September, 2000Google Scholar
- 8.Leroy B., Herliin, I., Cohen, L.D.: Multi-resolution algorithm for active contour models. In 12th Int. Conf. Analysis and Optimization of System. (1996) 58–65Google Scholar
- 12.Prince, J.L., Xu, C.: A new external force model for snakes. Proc. 1996 Image and Multidimensional Signal Processing Workshop. (1996) 30–31Google Scholar
- 16.Terzopoulos, D., Szeliski, R.: Tracking with Kalman snakes. in Active Vision, A. Blake and A. Yuille, Eds. Cambridge, MA: MIT Press, (1992) 3–20Google Scholar
- 17.Malladi, R., Sethian, J.A.: A real-time algorithm for medical shape recovery. in Proceedings of International Conference on Computer Vision. Mumbai, India. (1998) 304–310Google Scholar
- 18.Shah, J.: A common framework for curve evolution, segmentation and anisotropic diffusion. in Proceedings of International Conference on Computer Vision and Pattern Recognition: CVPR’96. June (1996) 136–142Google Scholar
- 19.Weickert, J.: Fast segmentation methods based on partial differential equations and the watershed transformation. P. Levi, R.-J. Ahlers, F. May, M. Schanz (Eds.), Mustererkennung 1998, Springer, Berlin. (1998) 93–100Google Scholar