Level set methods, distance function and image segmentation

In the study of level set methods, several significant problems were neglected all along, such as the existence, uniqueness and singularities of level set methods. In this article we give the proof that in a neighborhood of the initial zero level set, for the level set equations with the restriction of distance function, there exists a unique solution, which must be the signed distance junction with respect to the evolving surface. We also present the analysis of singular points effect on level set evolution and give an adaptive narrow banding algorithm. The detailed numerical analysis and a simplified definition for singular points are presented. We give an adaptive narrow banding algorithm, which avoids the singular points and is proved to be robust and efficient in segmentation of CT data and synthesized images.

[1]  J. Sethian,et al.  Crystal growth and dendritic solidification , 1992 .

[2]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Olivier D. Faugeras,et al.  Level Sets and Distance Functions , 2000, ECCV.

[4]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[5]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[6]  Demetri Terzopoulos,et al.  Topologically adaptable snakes , 1995, Proceedings of IEEE International Conference on Computer Vision.

[7]  Robert T. Schultz,et al.  Volumetric layer segmentation using coupled surfaces propagation , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[8]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[9]  J. Sethian 1 Advancing Interfaces : Level Set and Fast Marching Methods , 1999 .