Fast algorithm for hybrid region-based active contours optimisation

Active contours are usually based on the optimisation of energy functionals that are built to attract the curve towards the objects' boundaries. This study describes a hybrid region-based active contours technique that uses global means to define the global fitting energy and local means and variances to define the local fitting energy. The optimisation of the functional is performed by applying a sweeping-principle algorithm, which avoids solving any partial differential equation and removes the need for any stability conditions. Furthermore, sweeping-principle algorithm is not based on the computation of derivatives, which allows using a binary level set function during the minimisation process instead of the signed distance function, consequently this removes the need for the distance regularisation term, avoiding its subtle side effects and speeding up the optimisation process. Successful and accurate segmentation results are obtained on synthetic and real images with a significant gain in the CPU execution time when compared with the minimisation via the commonly used gradient descent method.

[1]  Xiaofeng Wang,et al.  An efficient local Chan-Vese model for image segmentation , 2010, Pattern Recognit..

[2]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[3]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

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

[5]  Yi Zhao,et al.  Split Bregman Method for Minimization of Fast Multiphase Image Segmentation Model for Inhomogeneous Images , 2015, J. Optim. Theory Appl..

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

[7]  Boying Wu,et al.  Split Bregman method for minimization of improved active contour model combining local and global information dynamically , 2012 .

[8]  Rémi Ronfard,et al.  Region-based strategies for active contour models , 1994, International Journal of Computer Vision.

[9]  Yang Yu,et al.  Active Contour Method Combining Local Fitting Energy and Global Fitting Energy Dynamically , 2010, ICMB.

[10]  Hai Min,et al.  A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement , 2015, Pattern Recognit..

[11]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[12]  Stelios Krinidis,et al.  Fuzzy Energy-Based Active Contours , 2009, IEEE Transactions on Image Processing.

[13]  Xue-Cheng Tai,et al.  A binary level set model and some applications to Mumford-Shah image segmentation , 2006, IEEE Transactions on Image Processing.

[14]  Po-Lei Lee,et al.  Fuzzy distribution fitting energy-based active contours for image segmentation , 2012 .

[15]  Ke Chen,et al.  A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity , 2016, Pattern Recognit..

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

[17]  Chunming Li,et al.  Implicit Active Contours Driven by Local Binary Fitting Energy , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Wufan Chen,et al.  Neighborhood Aided Implicit Active Contours , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Antonin Chambolle,et al.  A l1-Unified Variational Framework for Image Restoration , 2004, ECCV.

[20]  Gaofeng Meng,et al.  Level set evolution with locally linear classification for image segmentation , 2011, 2011 18th IEEE International Conference on Image Processing.

[21]  Hui Wang,et al.  An active contour model and its algorithms with local and global Gaussian distribution fitting energies , 2014, Inf. Sci..

[22]  Zheng Bao,et al.  Fast image inpainting and colorization by Chambolle's dual method , 2011, J. Vis. Commun. Image Represent..

[23]  Li Wang,et al.  Integrating local distribution information with level set for boundary extraction , 2010, J. Vis. Commun. Image Represent..

[24]  Po-Lei Lee,et al.  Global and local fuzzy energy-based active contours for image segmentation , 2012 .