Active contour model driven by Globally Signed Region Pressure Force

One of the most popular and widely used global active contour models (ACM) is the region-based ACM, which relies on the assumption of homogeneous intensity in the regions of interest. As a result, most often than not, when images violate this assumption the performance of this method is limited. Thus, handling images that contain foreground objects characterized by multiple intensity classes present a challenge. In this paper, we propose a novel active contour model based on a new Signed Pressure Force (SPF) function which we term Globally Signed Region Pressure Force (GSRPF). It is designed to incorporate, in a global fashion, the skewness of the intensity distribution of the region of interest (ROI). It can accurately modulate the signs of the pressure force inside and outside the contour, it can handle images with multiple intensity classes in the foreground, it is robust to additive noise, and offers high efficiency and rapid convergence. The proposed GSRPF is robust to contour initialization and has the ability to stop the curve evolution close to even ill-defined (weak) edges. Our model provides a parameter-free environment to allow minimum user intervention, and offers both local and global segmentation properties. Experimental results on several synthetic and real images demonstrate the high accuracy of the segmentation results in comparison to other methods adopted from the literature.

[1]  Guopu Zhu,et al.  Boundary-based image segmentation using binary level set method , 2007 .

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

[3]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

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

[5]  Shigang Liu,et al.  A local region-based Chan-Vese model for image segmentation , 2012, Pattern Recognit..

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

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

[8]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Kaleem Siddiqi,et al.  Flux maximizing geometric flows , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Li Chen,et al.  GACV: Geodesic-Aided C-V method , 2006, Pattern Recognit..

[11]  Bostjan Likar,et al.  A Review of Methods for Correction of Intensity Inhomogeneity in MRI , 2007, IEEE Transactions on Medical Imaging.

[12]  Rachid Deriche,et al.  Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  W. Clem Karl,et al.  Real-time tracking using level sets , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Chunming Li,et al.  Active contours driven by local Gaussian distribution fitting energy , 2009, Signal Process..

[15]  Kuo-Kai Shyu,et al.  Active contour with selective local or global segmentation for intensity inhomogeneous image , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[16]  Ping Wang,et al.  Local and Global Intensity Information Integrated Geodesic Model for Image Segmentation , 2012, 2012 International Conference on Computer Science and Electronics Engineering.

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