Recent Survey on Medical Image Segmentation

This chapter presents a survey on the techniques of medical image segmentation. Image segmentation methods are given in three groups based on image features used by the method. The advantages and disadvantages of the existing methods are evaluated, and the motivations to develop new techniques with respect to the addressed problems are given. Digital images and digital videos are pictures and films, respectively, which have been converted into a computer-readable binary format consisting of logical zeros and ones. An image is a still picture that does not change in time, whereas a video evolves in time and generally contains moving and/or changing objects. An important feature of digital images is that they are multidimensional signals, i.e., they are functions of more than a single variable. In the classical study of the digital signal processing the signals are usually one-dimensional functions of time. Images however, are functions of two, and perhaps three space dimensions in case of colored images, whereas a digital video as a function includes a third (or fourth) time dimension as well. A consequence of this is that digital image processing, meaning that significant computational and storage resources are required. Recent Survey on Medical Image Segmentation

[1]  S. Casciaro,et al.  Fully Automatic Liver Segmentation through Graph-Cut Technique , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Qianjin Feng,et al.  A non-parametric method based on NBNN for automatic detection of liver lesion in CT images , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[3]  Joachim Hornegger,et al.  Automatic Detection and Segmentation of Focal Liver Lesions in Contrast Enhanced CT Images , 2010, 2010 20th International Conference on Pattern Recognition.

[4]  Heinz Hügli,et al.  MAPS: Multiscale Attention-Based PreSegmentation of Color Images , 2003, Scale-Space.

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

[6]  Jianhua Liu,et al.  Liver Cancer CT Image Segmentation Methods Based on Watershed Algorithm , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[7]  Li Ma,et al.  Liver Segmentation Based on Expectation Maximization and Morphological Filters in CT Images , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[8]  M. Usman Akram,et al.  An automated System for Liver CT Enhancement and Segmentation , 2010 .

[9]  Yixin Chen,et al.  Image Categorization by Learning and Reasoning with Regions , 2004, J. Mach. Learn. Res..

[10]  Vipin Chaudhary,et al.  Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis , 2011, Medical Imaging.

[11]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Edward J. Delp,et al.  The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[13]  Hai Tao,et al.  Fast Multi-scale Template Matching Using Binary Features , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

[14]  Thomas Martin Deserno,et al.  Bildverarbeitung für die Medizin: Grundlagen, Modelle, Methoden, Anwendungen , 1997, Bildverarbeitung für die Medizin.

[15]  Aly A. Farag,et al.  3D vertebrae segmentation using graph cuts with shape prior constraints , 2010, 2010 IEEE International Conference on Image Processing.

[16]  In-So Kweon,et al.  Automatic edge detection method for the mobile robot application , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[17]  Defeng Wang,et al.  Automatic liver segmentation in CT images based on Support Vector Machine , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[18]  Mariano Alcañiz Raya,et al.  Liver Segmentation on CT Images. A Fast Computational Method Based on 3D Morphology and a Statistical Filter , 2013, IWBBIO.

[19]  Khalid M. Amin,et al.  Fully automatic liver tumor segmentation from abdominal CT scans , 2010, The 2010 International Conference on Computer Engineering & Systems.

[20]  Vipin Chaudhary,et al.  Segmentation of the Liver from Abdominal CT Using Markov Random Field Model and GVF Snakes , 2008, 2008 International Conference on Complex, Intelligent and Software Intensive Systems.

[21]  Laurent D. Cohen,et al.  Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests , 2012, MICCAI.

[22]  D. F. Watson Computing the n-Dimensional Delaunay Tesselation with Application to Voronoi Polytopes , 1981, Comput. J..

[23]  Dhananjay Kumar,et al.  An improved Bayesian Network Model Based Image Segmentation in detection of lung cancer , 2014, 2014 International Conference on Recent Trends in Information Technology.

[24]  Xiangrong Zhou,et al.  Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image , 2013, Comput. Math. Methods Medicine.

[25]  Mohiy M. Hadhoud,et al.  A fully automatic and efficient technique for liver segmentation from abdominal CT images , 2010, 2010 The 7th International Conference on Informatics and Systems (INFOS).

[26]  Ben Glocker,et al.  Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans , 2012, MICCAI.

[27]  Rupali Balpande,et al.  Liver segmentation of CT scan images using K means algorithm , 2013, 2013 International Conference on Advanced Electronic Systems (ICAES).

[28]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[29]  Leo Joskowicz,et al.  An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation , 2008, International Journal of Computer Assisted Radiology and Surgery.

[30]  William A. Barrett,et al.  Intelligent scissors for image composition , 1995, SIGGRAPH.

[31]  Jun Zhou,et al.  Computer Vision and Pattern Recognition in Environmental Informatics , 2015, CVPR 2015.

[32]  Min Tan,et al.  Support Vector Machines (SVM) for Color Image Segmentation with Applications to Mobile Robot Localization Problems , 2005, ICIC.

[33]  Andrew Blake,et al.  Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography , 2009, FIMH.

[34]  Xiao Song,et al.  Automatic Liver Segmentation from CT Images Using Adaptive Fast Marching Method , 2013, 2013 Seventh International Conference on Image and Graphics.

[35]  A. Govardhan,et al.  Active Contours and Image Segmentation: The Current State Of the Art , 2012 .

[36]  Javad Alirezaie,et al.  Neural network based segmentation of magnetic resonance images of the brain , 1995 .

[37]  Nikolaos G. Bourbakis,et al.  A neural network-based segmentation tool for color images , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[38]  Jorge Azorín López,et al.  A Compilation of Methods and Datasets for Group and Crowd Action Recognition , 2017, Int. J. Comput. Vis. Image Process..

[39]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[40]  Xiaobo Li,et al.  Adaptive image region-growing , 1994, IEEE Trans. Image Process..

[41]  Frank Y. Shih,et al.  Fast Euclidean distance transformation in two scans using a 3 × 3 neighborhood , 2004, Comput. Vis. Image Underst..

[42]  Jong-An Park,et al.  Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing , 2005, ICNC.

[43]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[45]  M. Jayanthi,et al.  Extracting the Liver and Tumor from Abdominal CT Images , 2014, 2014 Fifth International Conference on Signal and Image Processing.

[46]  Noboru Niki,et al.  Extraction of liver volumetry based on blood vessel from the portal phase CT dataset , 2012, Medical Imaging.

[47]  Yilin Chang,et al.  Improved Method for Gradient-Threshold Edge Detector Based on HVS , 2005, CIS.

[48]  Josef Kittler,et al.  Region growing: a new approach , 1998, IEEE Trans. Image Process..

[49]  Khalid M. Amin,et al.  Automatic liver tumor segmentation from CT scans with knowledge-based constraints , 2010, 2010 5th Cairo International Biomedical Engineering Conference.

[50]  Dorin Comaniciu,et al.  Multi-stage osteolytic spinal bone lesion detection from CT data with internal sensitivity control , 2012, Medical Imaging.

[51]  Tony F. Chan,et al.  Image processing and analysis - variational, PDE, wavelet, and stochastic methods , 2005 .

[52]  Azriel Rosenfeld,et al.  Image enhancement and thresholding by optimization of fuzzy compactness , 1988, Pattern Recognit. Lett..

[53]  Aboul Ella Hassanien,et al.  Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[54]  Josiane Zerubia,et al.  Markov Random Fields in Image Segmentation , 2012, Found. Trends Signal Process..

[55]  Mohamed Ali Mahjoub,et al.  Automatic liver segmentation method in CT images , 2012, ArXiv.

[56]  F. Mekhalfa,et al.  Unsupervised Algorithm for Radiographic Image Segmentation Based on the Gaussian Mixture Model , 2007, EUROCON 2007 - The International Conference on "Computer as a Tool".

[57]  James C. Bezdek,et al.  Prototype classification and feature selection with fuzzy sets , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[58]  Ben Glocker,et al.  Robust Registration of Longitudinal Spine CT , 2014, MICCAI.

[59]  Xing Zhang,et al.  Interactive liver tumor segmentation from ct scans using support vector classification with watershed , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[60]  Lianfen Huang,et al.  Liver segmentation in CT images based on region-scalable fitting model , 2013, 2013 International Conference on Anti-Counterfeiting, Security and Identification (ASID).

[61]  Clark F. Olson Maximum-Likelihood Image Matching , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[62]  Aly A. Farag,et al.  Modified fuzzy c-mean in medical image segmentation , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[63]  S. S. Kumar,et al.  Automatic Segmentation of Liver and Tumor for CAD of Liver , 2011 .

[64]  Gerald Schaefer,et al.  Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension , 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

[65]  Hagai Attias,et al.  Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.

[66]  Mohammed Saeed,et al.  Maximum likelihood parameter estimation of mixture models and its application to image segmentation and restoration , 1997 .

[67]  T. Moon The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[68]  John Alejandro Castro-Vargas,et al.  Accelerating Deep Action Recognition Networks for Real-Time Applications , 2019, Int. J. Comput. Vis. Image Process..

[69]  Ben Glocker,et al.  Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations , 2013, MICCAI.

[70]  Xiaolin Chen,et al.  Insect recognition using sparse coding and decision fusion , 2015 .

[71]  Antonio Criminisi,et al.  Regression Forests for Efficient Anatomy Detection and Localization in CT Studies , 2010, MCV.

[72]  Zhi-Kai Huang,et al.  Segmentation of Color Image Using EM algorithm in HSV Color Space , 2007, 2007 International Conference on Information Acquisition.

[73]  Noboru Niki,et al.  Blood vessel-based liver segmentation through the portal phase of a CT dataset , 2013, Medical Imaging.