An improved active contour model driven by region-scalable and local Gaussian-distribution fitting energy

Images with low contrast, overlapped noise and intensity inhomogeneity of multiple objects make many existing level set methods disabled for image segmentation. To address the problem, an improved active contour model is proposed, driving by region-scalable and local Gaussian-distribution fitting energy for image segmentation. Firstly, we classify regions with similar intensity by utilizing the means and variances of local image intensities. Secondly, we define a new edge stopping functional to robustly capture the boundaries of multiple objects. Finally, we utilize LoG energy term to catch edge information and smooth the homogeneous regions, which can be optimized by an energy function. Experiments results on real and synthetic images validate that our method is faster, robuster and higher accuracy than other major region-based methods for images with multiple objects.

[1]  Sen Qian,et al.  Medical image segmentation based on FCM and Level Set algorithm , 2016, 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS).

[2]  Hamid Laga,et al.  The Influence of Object Shape on the Convergence of Active Contour Models for Image Segmentation , 2016, Comput. J..

[3]  P. Liang,et al.  Hepatic vessel segmentation using variational level set combined with non-local robust statistics. , 2017, Magnetic resonance imaging.

[4]  Manassanan Srikham,et al.  Active contours segmentation with edge based and local region based , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

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

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

[7]  Lei Zhang,et al.  Active contours driven by local image fitting energy , 2010, Pattern Recognit..

[8]  Hui Wang,et al.  An efficient active contour with Gaussian distribution fitting energy , 2013, 2013 IEEE Third International Conference on Information Science and Technology (ICIST).

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

[10]  Zhenyu He,et al.  Robust Object Tracking via Key Patch Sparse Representation , 2017, IEEE Transactions on Cybernetics.

[11]  Daniel L. Rubin,et al.  Adaptive local window for level set segmentation of CT and MRI liver lesions , 2016, Medical Image Anal..

[12]  Zhenyu He,et al.  Connected Component Model for Multi-Object Tracking , 2016, IEEE Transactions on Image Processing.

[13]  Yan Wang,et al.  Active contours driven by weighted region-scalable fitting energy based on local entropy , 2012, Signal Process..

[14]  Bin Fang,et al.  Multi-scale B-spline level set segmentation based on Gaussian kernel equalization , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[15]  Zhi Xu,et al.  A two-stage image segmentation via global and local region active contours , 2016, Neurocomputing.

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

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

[18]  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).

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

[20]  Yi Wang,et al.  Automatic Liver Lesion Segmentation in CT Combining Fully Convolutional Networks and Non-negative Matrix Factorization , 2017, BIVPCS/POCUS@MICCAI.

[21]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[22]  David Zhang,et al.  A Level Set Approach to Image Segmentation With Intensity Inhomogeneity , 2016, IEEE Transactions on Cybernetics.

[23]  Xinge You,et al.  Automatic Ear Landmark Localization, Segmentation, and Pose Classification in Range Images , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Linfang Xiao,et al.  Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation , 2017, Signal Process..

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

[26]  Yi Wang,et al.  A novel variational method for liver segmentation based on statistical shape model prior and enforced local statistical feature , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[27]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[28]  M. Li,et al.  Adaptive Regularized Level Set Method for Weak Boundary Object Segmentation , 2012 .

[29]  Bin Fang,et al.  B-Spline based globally optimal segmentation combining low-level and high-level information , 2018, Pattern Recognit..

[30]  Qinmu Peng,et al.  Segmentation of retinal blood vessels using the radial projection and semi-supervised approach , 2011, Pattern Recognit..

[31]  Amar Mitiche,et al.  Variational and Level Set Methods in Image Segmentation , 2010 .

[32]  Jian Yang,et al.  Inhomogeneity-embedded active contour for natural image segmentation , 2015, Pattern Recognit..