On the incorporation of shape priors into geometric active contours

A novel model for boundary determination that incorporates prior shape information into geometric active contours is presented. The basic idea of this model is to minimize the energy functional depending on the information of the image gradient and the shape of interest, so that the boundary of the object can be captured either by higher magnitude of the image gradient or by the prior knowledge of its shape. The level set form of the proposed model is also provided. We present our experimental results on some synthetic images, functional MR brain images, and ultrasound images for which the existing active contour methods are not applicable. The existence of the solution to the proposed minimization problem is also discussed.

[1]  E. Giusti.  Minimal surfaces and functions of bounded variation , 1977 .

[2]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[3]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[4]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[5]  S. Osher,et al.  A level set approach for computing solutions to incompressible two-phase flow , 1994 .

[6]  Anthony J. Yezzi,et al.  Gradient flows and geometric active contour models , 1995, Proceedings of IEEE International Conference on Computer Vision.

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

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

[9]  Surendra Ranganath,et al.  Contour extraction from cardiac MRI studies using snakes , 1995, IEEE Trans. Medical Imaging.

[10]  J. Sethian,et al.  A geometric approach to segmentation and analysis of 3D medical images , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[11]  T. Chan,et al.  A Variational Level Set Approach to Multiphase Motion , 1996 .

[12]  Anthony J. Yezzi,et al.  A geometric snake model for segmentation of medical imagery , 1997, IEEE Transactions on Medical Imaging.

[13]  J S Hyde,et al.  Contour‐based registration technique to differentiate between task‐activated and head motion‐induced signal variations in fMRI , 1997, Magnetic resonance in medicine.

[14]  Lawrence H. Staib,et al.  Boundary finding with correspondence using statistical shape models , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[15]  Timothy F. Cootes,et al.  Atlas matching using active appearance models , 1999 .

[16]  J. A. Sethian,et al.  Fast Marching Methods , 1999, SIAM Rev..

[17]  Timothy F. Cootes,et al.  A Unified Framework for Atlas Matching Using Active Appearance Models , 1999, IPMI.

[18]  Olivier D. Faugeras,et al.  Statistical shape influence in geodesic active contours , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[19]  Tony F. Chan,et al.  Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..

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