Image segmentation using the level set and improved-variation smoothing

To deal with the inhomogeneous intensity in real images, we propose image smoothing conditions for image segmentation.According to the smoothing conditions, we improve the total variation and propose a new smoothing function which smoothes the inhomogeneous intensity and enhances edge.Combine the classical level set, the new image segmentation model is proposed to deal with the inhomogeneous intensity in real images.We give the convergence condition of image smoothing according to the confidence level of segmentation sub-regions. Traditional active contour models perform poorly on real images with inhomogeneous sub-regions. In order to overcome this limitation, this paper has proposed a novel segmentation algorithm. Firstly, analyzing the smoothing conditions for image segmentation, we construct a smoothing function with improved total variation. This function can smooth the inhomogeneous sub-regions, preserve the strong edges and enhance the weak edges. Then, the level set is employed to segment the smoothing component using the smoothing function. Lastly, according to the confidence level of segmentation sub-regions, we add a convergence condition to the smoothing to prevent the segmentation curve from vanishing. Experimental results indicate that this model is insensitive to noise and can deal with inhomogeneous intensity.

[1]  Tony F. Chan,et al.  The digital TV filter and nonlinear denoising , 2001, IEEE Trans. Image Process..

[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]  Fang Liu,et al.  A Normalized Local Binary Fitting Model for Image Segmentation , 2012, 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems.

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

[5]  Stanley Osher,et al.  Image Denoising and Decomposition with Total Variation Minimization and Oscillatory Functions , 2004, Journal of Mathematical Imaging and Vision.

[6]  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).

[7]  G. M.,et al.  Partial Differential Equations I , 2023, Applied Mathematical Sciences.

[8]  T. N. Janakiraman,et al.  Image Segmentation Based on Minimal Spanning Tree and Cycles , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[9]  Zhiheng Zhou,et al.  Global minimization of adaptive local image fitting energy for image segmentation , 2014 .

[10]  Anthony J. Yezzi,et al.  Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification , 2001, IEEE Trans. Image Process..

[11]  Sangwook Kim,et al.  MPEG-4 STUDIO: An Object-Based Authoring System for MPEG-4 Contents , 2004, Multimedia Tools and Applications.

[12]  Xuelong Li,et al.  Improving Level Set Method for Fast Auroral Oval Segmentation , 2014, IEEE Transactions on Image Processing.

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

[14]  Baohua Zhang,et al.  The study and application of the improved region growing algorithm for liver segmentation , 2014 .

[15]  Reiner Lenz,et al.  Modified Gradient Search for Level Set Based Image Segmentation , 2013, IEEE Transactions on Image Processing.

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

[17]  Fang Liu,et al.  Active contours driven by normalized local image fitting energy , 2014, Concurr. Comput. Pract. Exp..

[18]  L. Vese,et al.  A level set algorithm for minimizing the Mumford-Shah functional in image processing , 2001, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision.

[19]  Muhammad Khan,et al.  A Survey: Image Segmentation Techniques , 2014 .

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

[21]  Xuelong Li,et al.  Interactive Segmentation Using Constrained Laplacian Optimization , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

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

[23]  Xianghua Xie,et al.  Segmentation of biomedical images using active contour model with robust image feature and shape prior , 2013, International journal for numerical methods in biomedical engineering.

[24]  Nanning Zheng,et al.  Joint Segmentation and Recognition of Categorized Objects From Noisy Web Image Collection , 2014, IEEE Transactions on Image Processing.