Robust Skeleton Extraction of Gray Images based on Level Set Approach

The skeleton of an image object is a simplified representation, which is of great significance for the image recognition and matching. To obtain a smooth and accurate skeleton of a specified object in the gray image, this paper provides a unified framework by combining the level set idea with the gradient module method. The intrinsic procedure involves three steps. First, an energy function is given by virtue of the statistical intensity disparity between the sample points and the object, and then a novel segmentation model is proposed to extract any specified objects in the gray image by the variational method. Moreover, the segmentation model is further improved to be suited to the texture image. Second, in order to find an accurate position of the skeleton, a searching algorithm for the endpoints of the skeleton is shown based on the segmentation result. Finally, an improved skeleton extraction algorithm is given via the shortest path connection approach. Some examples show the robustness and insensitivity of the presented algorithm to the perturbation and noise, respectively.

[1]  Kai-Yun Chan,et al.  The multiresolution gradient vector field skeleton , 2007, Pattern Recognit..

[2]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jing Xu,et al.  Skeleton extraction of cerebral vascular image based on topology node , 2010, 2010 3rd International Conference on Biomedical Engineering and Informatics.

[4]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[5]  Wooi-Boon Goh,et al.  Strategies for shape matching using skeletons , 2008, Comput. Vis. Image Underst..

[6]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[7]  Hongkai Zhao,et al.  A fast sweeping method for Eikonal equations , 2004, Math. Comput..

[8]  Gisela Klette.  Skeletons in Digital Image Processing , 2002 .

[9]  Ki-Sang Hong,et al.  A pseudo-distance map for the segmentation-free skeletonization of gray-scale images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Benjamin B. Kimia,et al.  Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model , 2011, International Journal of Computer Vision.

[11]  Jing Xu,et al.  DSA Image Blood Vessel Skeleton Extraction Based on Anti-concentration Diffusion and Level Set Method , 2009 .

[12]  Arjan Kuijper,et al.  Alternative 2D Shape Representations using the Symmetry Set , 2006, Journal of Mathematical Imaging and Vision.

[13]  Mark S. Nixon,et al.  Feature Extraction and Image Processing , 2002 .

[14]  Rachid Deriche,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Colour, Texture, and Motion in Level Set Based Segmentation and Tracking Colour, Texture, and Motion in Level Set Based Segmentation and Tracking , 2022 .

[15]  Manuel Graña,et al.  A Pruning Algorithm for Stable Voronoi Skeletons , 2011, Journal of Mathematical Imaging and Vision.

[16]  John W. Fisher,et al.  Submitted to Ieee Transactions on Image Processing a Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution , 2022 .

[17]  D. Mumford,et al.  The role of the primary visual cortex in higher level vision , 1998, Vision Research.

[18]  Zeyun Yu,et al.  A segmentation-free approach for skeletonization of gray-scale images via anisotropic vector diffusion , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[19]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[20]  Kaleem Siddiqi,et al.  The Hamilton-Jacobi skeleton , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  Nahum Kiryati,et al.  On Symmetry, Perspectivity, and Level-Set-Based Segmentation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Hongmei Xie,et al.  Texture image segmentation using fully global minimization active contour model , 2011, 2011 International Conference on Multimedia Technology.

[23]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[24]  Edwin R. Hancock,et al.  A skeletal measure of 2D shape similarity , 2001, Comput. Vis. Image Underst..

[25]  Kai-Yun Chan,et al.  Structural and Textural Skeletons for Noisy Shapes , 2005, ISVC.

[26]  Benjamin B. Kimia,et al.  On the Local Form and Transitions of Symmetry Sets, Medial Axes, and Shocks , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.