A variational segmentation framework using active contours and thresholding

Segmentation involves separating distinct regions in an image. In this note, we present a novel variational approach to perform this task. We propose an energy functional that naturally combines two segmentation techniques usually applied separately: intensity thresholding and geometric active contours. Although our method can deal with more complex image statistics, intensity averages are used to separate regions, in this present work. The proposed approach affords interesting properties that can lead to sensible segmentation results.

[1]  R. Deriche,et al.  A variational framework for active and adaptative segmentation of vector valued images , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[2]  Anthony J. Yezzi,et al.  A statistical approach to snakes for bimodal and trimodal imagery , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Alan S. Willsky,et al.  Medical image segmentation via coupled curve evolution equations with global constraints , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[4]  John W. Fisher,et al.  Nonparametric methods for image segmentation using information theory and curve evolution , 2002, Proceedings. International Conference on Image Processing.

[5]  P. Olver,et al.  Conformal curvature flows: From phase transitions to active vision , 1996, ICCV 1995.

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

[7]  Jean-Michel Morel,et al.  Variational methods in image segmentation , 1995 .

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

[9]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[10]  Yogesh Rathi,et al.  Shape-Based Approach to Robust Image Segmentation using Kernel PCA , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Nikos Paragios,et al.  Handbook of Mathematical Models in Computer Vision , 2005 .

[12]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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