A new level set method for inhomogeneous image segmentation

Intensity inhomogeneity often appears in medical images, such as X-ray tomography and magnetic resonance (MR) images, due to technical limitations or artifacts introduced by the object being imaged. It is difficult to segment such images by traditional level set based segmentation models. In this paper, we propose a new level set method integrating local and global intensity information adaptively to segment inhomogeneous images. The local image information is associated with the intensity difference between the average of local intensity distribution and the original image, which can significantly increase the contrast between foreground and background. Thus, the images with intensity inhomogeneity can be efficiently segmented. What is more, to avoid the re-initialization of the level set function and shorten the computational time, a simple and fast level set evolution formulation is used in the numerical implementation. Experimental results on synthetic images as well as real medical images are shown in the paper to demonstrate the efficiency and robustness of the proposed method.

[1]  L.-K. Shark,et al.  Medical Image Segmentation Using New Hybrid Level-Set Method , 2008, 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics.

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

[3]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[4]  Suyash P. Awate,et al.  Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics , 2006, ECCV.

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

[6]  Michael Brady,et al.  Estimating the bias field of MR images , 1997, IEEE Transactions on Medical Imaging.

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

[8]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

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

[10]  Chunming Li,et al.  Brain MR Image Segmentation Using Local and Global Intensity Fitting Active Contours/Surfaces , 2008, MICCAI.

[11]  Xiao-Feng Wang,et al.  A Level Set Based Segmentation Method for Images with Intensity Inhomogeneity , 2009, ICIC.

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

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

[14]  Chunming Li,et al.  Computerized Medical Imaging and Graphics Active Contours Driven by Local and Global Intensity Fitting Energy with Application to Brain Mr Image Segmentation , 2022 .

[15]  Martin Styner,et al.  Parametric estimate of intensity inhomogeneities applied to MRI , 2000, IEEE Transactions on Medical Imaging.

[16]  Jayant Shah,et al.  A common framework for curve evolution, segmentation and anisotropic diffusion , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Chunming Li,et al.  A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity , 2008, MICCAI.

[18]  Koenraad Van Leemput,et al.  Automated model-based bias field correction of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[19]  Yunjie Chen,et al.  Image segmentation and bias correction via an improved level set method , 2011, Neurocomputing.

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

[21]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[22]  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.

[23]  T. Chan,et al.  Multiple level set methods with applications for identifying piecewise constant functions , 2004 .

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

[25]  S. Osher,et al.  Geometric Level Set Methods in Imaging, Vision, and Graphics , 2011, Springer New York.

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

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

[28]  Chunming Li,et al.  MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework , 2009, IPMI.

[29]  P. Souganidis,et al.  Phase Transitions and Generalized Motion by Mean Curvature , 1992 .

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

[31]  Théodore Papadopoulo,et al.  Efficient Segmentation of Piecewise Smooth Images , 2007, SSVM.

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

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

[34]  S. Esedoglu,et al.  Threshold dynamics for the piecewise constant Mumford-Shah functional , 2006 .

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