Improved Snake Model for Fast Image Segmentation

This paper presents an improved snake model (ISM) effective for performing fast image segmentation. The work takes advantage of two well-known snake models of Balloon (Cohen, 1991) and GGVF (generalized gradient vector flow) (Xu and prince, 1998). The Balloon model is well known for its capability of fast locating object boundary using an extra pressure force field, but it may easily cause the contour to overwhelm the boundary. The more accurate GGVF model, however, requires lengthy computation time due to the need to specify a relatively large range in order to cover all the control points inside the range. Our goal is to obtain the exact field provided by GGVF while at the same time to expand the capture range with the Balloon pressure force. To this end, in ISM the force fields of Balloon and GGVT are integrated and a dynamic scheme for setting the control points is employed to avoid the time-consuming "re-sampling process" in both GVF (gradient vector flow) and GGVF. The key attribute of ISM is that it can reduce the number of training iterations while maintaining the capability of pushing the capture range toward the gradient map. Empirical results show that the proposed model can deliver the same performance level of segmentation using less computation time than "GGVF" does.

[1]  James M. Keller,et al.  Snakes on the Watershed , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Benjamin B. Kimia,et al.  Shapes, shocks, and deformations I: The components of two-dimensional shape and the reaction-diffusion space , 1995, International Journal of Computer Vision.

[3]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[4]  Harald Haas,et al.  Asilomar Conference on Signals, Systems, and Computers , 2006 .

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

[6]  A. Yezzi,et al.  On the relationship between parametric and geometric active contours , 2000, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).

[7]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

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

[9]  Jenq-Neng Hwang,et al.  Neural networks for intelligent multimedia processing , 1998 .

[10]  Scott T. Acton,et al.  Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation , 2003, IEEE Transactions on Medical Imaging.

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

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

[13]  Jerry L Prince,et al.  Global Optimality of Gradient Vector Flow , 2000 .

[14]  Guillermo Sapiro,et al.  Affine invariant scale-space , 1993, International Journal of Computer Vision.

[15]  Xianghua Xie,et al.  RAGS: region-aided geometric snake , 2004, IEEE Transactions on Image Processing.

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

[17]  D CohenLaurent.  On active contour models and balloons , 1991 .

[18]  Christophe Chesnaud,et al.  Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Max A. Viergever,et al.  A discrete dynamic contour model , 1995, IEEE Trans. Medical Imaging.

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

[21]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[22]  P. Lions,et al.  Axioms and fundamental equations of image processing , 1993 .