A survey of level set method for image segmentation with intensity inhomogeneity

Image segmentation is a fundamental task in computer vision and image processing. Due to the presence of high noise, low resolution and intensity inhomogeneity, it is still a difficult problem in the practical applications. Level set methods have been widely used in image processing and computer vision. During the past decades, many models based on level set methods have been proposed to solve image segmentation with intensity inhomogeneity. It is necessary to conduct a comprehensive review and comparison of these models. Specifically, level set methods can be categorized into two groups, including edge-based level set methods (EBLSMs) and region-based level set methods (RBLSMs). This paper reviews some of the recent advances in EBLSMs and RBLSMs for segmenting image with intensity inhomogeneity. Their advantages and disadvantages are discussed in an objective point of view, and their performance is compared on image segmentation with intensity inhomogeneity. Finally, this paper further explores and discusses some open questions in segmenting images with intensity inhomogeneity.

[1]  Yaozong Gao,et al.  LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images , 2015, NeuroImage.

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

[3]  James A. Sethian,et al.  Level set and fast marching methods in image processing and computer vision , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[4]  Olivier Faugeras,et al.  Reconciling Distance Functions and Level Sets , 2000, J. Vis. Commun. Image Represent..

[5]  Alexander J. Smola,et al.  Parallelized Stochastic Gradient Descent , 2010, NIPS.

[6]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Yong Gan,et al.  An active contour model based on fused texture features for image segmentation , 2015, Neurocomputing.

[8]  Guifang Fu,et al.  Mapping morphological shape as a high-dimensional functional curve , 2017, Briefings Bioinform..

[9]  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..

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

[11]  Darren J. Wilkinson,et al.  Bayesian methods in bioinformatics and computational systems biology , 2006, Briefings Bioinform..

[12]  Mohammed Elmogy,et al.  Brain tumor segmentation based on a hybrid clustering technique , 2015 .

[13]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

[14]  Bo Du,et al.  Exploring Locally Adaptive Dimensionality Reduction for Hyperspectral Image Classification: A Maximum Margin Metric Learning Aspect , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Adam Tauman Kalai,et al.  Online convex optimization in the bandit setting: gradient descent without a gradient , 2004, SODA '05.

[16]  Yunjie Chen,et al.  An improved level set method for brain MR images segmentation and bias correction , 2009, Comput. Medical Imaging Graph..

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

[18]  Qiang Wang,et al.  Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints , 2016, Neurocomputing.

[19]  Mila Nikolova,et al.  Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models , 2006, SIAM J. Appl. Math..

[20]  Weibin Liu,et al.  An improved edge-based level set method combining local regional fitting information for noisy image segmentation , 2017, Signal Process..

[21]  Xun Wang,et al.  A comparative study of deformable contour methods on medical image segmentation , 2008, Image Vis. Comput..

[22]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ralph R. Martin,et al.  A Comparative Study of Algorithms for Realtime Panoramic Video Blending , 2016, IEEE Transactions on Image Processing.

[24]  Jens Rittscher,et al.  Spatio-temporal cell cycle phase analysis using level sets and fast marching methods , 2009, Medical Image Anal..

[25]  Lurng-Kuo Liu,et al.  A block-based gradient descent search algorithm for block motion estimation in video coding , 1996, IEEE Trans. Circuits Syst. Video Technol..

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

[27]  G. Allaire,et al.  Structural optimization using sensitivity analysis and a level-set method , 2004 .

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

[29]  William B. Langdon,et al.  An overview of image-processing methods for Affymetrix GeneChips , 2007, Briefings Bioinform..

[30]  Manfred K. Warmuth,et al.  Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..

[31]  David Ebenezer,et al.  A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises , 2007, IEEE Signal Processing Letters.

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

[33]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

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

[35]  David Zhang,et al.  Reinitialization-Free Level Set Evolution via Reaction Diffusion , 2011, IEEE Transactions on Image Processing.

[36]  Ruimin Hu,et al.  Geometrically Based Linear Iterative Clustering for Quantitative Feature Correspondence , 2016, Comput. Graph. Forum.

[37]  Mark Sussman,et al.  An Efficient, Interface-Preserving Level Set Redistancing Algorithm and Its Application to Interfacial Incompressible Fluid Flow , 1999, SIAM J. Sci. Comput..

[38]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[39]  Tony F. Chan,et al.  Level set based shape prior segmentation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[40]  Nikolas Mitrou,et al.  A Web-based Database System for Providing Technical Information on ATM Networking Platforms , 2003 .

[41]  Michael Yu Wang,et al.  Shape and topology optimization of compliant mechanisms using a parameterization level set method , 2007, J. Comput. Phys..

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

[43]  Emmanuel Maitre,et al.  Applications of level set methods in computational biophysics , 2009, Math. Comput. Model..

[44]  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.

[45]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[46]  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 .

[47]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[48]  S. Osher,et al.  Regular Article: A PDE-Based Fast Local Level Set Method , 1999 .

[49]  J. Gore,et al.  Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. , 2014, Magnetic resonance imaging.

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

[51]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

[52]  Yiteng Pan,et al.  A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation , 2019, Multimedia Tools and Applications.

[53]  Yiteng Pan,et al.  A novel region-based active contour model via local patch similarity measure for image segmentation , 2018, Multimedia Tools and Applications.

[54]  Byung Chul Kim,et al.  Feature-based simplification of boundary representation models using sequential iterative volume decomposition , 2014, Comput. Graph..

[55]  Alfred M. Bruckstein,et al.  Finding Shortest Paths on Surfaces Using Level Sets Propagation , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[56]  Zhe Zhu,et al.  Deep Learning for identifying radiogenomic associations in breast cancer , 2017, Comput. Biol. Medicine.

[57]  Paul Tseng,et al.  A coordinate gradient descent method for nonsmooth separable minimization , 2008, Math. Program..

[58]  Byunghan Lee,et al.  Deep learning in bioinformatics , 2016, Briefings Bioinform..

[59]  Xiaoming Wang,et al.  A level set method for structural topology optimization , 2003 .

[60]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[61]  Wei Jia,et al.  A novel dual minimization based level set method for image segmentation , 2016, Neurocomputing.

[62]  Ian M. Mitchell,et al.  A hybrid particle level set method for improved interface capturing , 2002 .

[63]  S. Osher,et al.  A level set approach for computing solutions to incompressible two-phase flow , 1994 .

[64]  Yiteng Pan,et al.  A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation , 2018, Multimedia Tools and Applications.

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

[66]  Hai Min,et al.  Multi-scale local region based level set method for image segmentation in the presence of intensity inhomogeneity , 2015, Neurocomputing.

[67]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[69]  Duhwan Mun,et al.  Enhanced volume decomposition minimizing overlapping volumes for the recognition of design features , 2015, Journal of Mechanical Science and Technology.

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

[71]  Weibin Liu,et al.  A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation , 2019, J. Vis. Commun. Image Represent..

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

[73]  Siu Kai Choy,et al.  Fuzzy model-based clustering and its application in image segmentation , 2017, Pattern Recognit..

[74]  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.

[75]  Qiang Chen,et al.  Robust noise region-based active contour model via local similarity factor for image segmentation , 2017, Pattern Recognit..

[76]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[77]  S. Osher,et al.  Level set methods: an overview and some recent results , 2001 .

[78]  Chunxia Xiao,et al.  Surface Reconstruction via Fusing Sparse-Sequence of Depth Images , 2018, IEEE Transactions on Visualization and Computer Graphics.

[79]  Frank Y. Shih,et al.  Retinal vessels segmentation based on level set and region growing , 2014, Pattern Recognit..

[80]  G. Barles,et al.  Front propagation and phase field theory , 1993 .

[81]  Qiang Chen,et al.  Active contours driven by local likelihood image fitting energy for image segmentation , 2015, Inf. Sci..

[82]  Suk Ho Lee,et al.  Level set-based bimodal segmentation with stationary global minimum , 2006, IEEE Transactions on Image Processing.

[83]  Wei Jia,et al.  An Intensity-Texture model based level set method for image segmentation , 2015, Pattern Recognit..

[84]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[85]  Lin Yang,et al.  An Automatic Learning-Based Framework for Robust Nucleus Segmentation , 2016, IEEE Transactions on Medical Imaging.

[86]  Jian Huang,et al.  Penalized feature selection and classification in bioinformatics , 2008, Briefings Bioinform..

[87]  Jean Meunier,et al.  Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions , 2006, IEEE Transactions on Medical Imaging.

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

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

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

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

[92]  Zhe Zhu,et al.  Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data , 2018, Medical Imaging.

[93]  Andrew Blake,et al.  Sparse Finite Elements for Geodesic Contours with Level-Sets , 2004, ECCV.

[94]  Thomas Brox,et al.  Level Set Based Image Segmentation with Multiple Regions , 2004, DAGM-Symposium.

[95]  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..