Hybrid Soft Computing for Image Segmentation

This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.

[1]  N. Sri Madhava Raja,et al.  Improved PSO Based Multi-level Thresholding for Cancer Infected Breast Thermal Images Using Otsu , 2015 .

[2]  Rosalina Abdul Salam,et al.  Blood cell image segmentation using hybrid K-means and median-cut algorithms , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[3]  N. M. Dung,et al.  Fluid replacement in dengue shock syndrome: a randomized, double-blind comparison of four intravenous-fluid regimens. , 1999, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[4]  Ivona Brajevic,et al.  Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding , 2014 .

[5]  Françoise Argoul,et al.  Wavelet-based multifractal analysis of dynamic infrared thermograms to assist in early breast cancer diagnosis , 2014, Front. Physiol..

[6]  Sung Youn Choi,et al.  Dengue Fever Mimicking Acute Appendicitis: A Case Report , 2009 .

[7]  Aura Conci,et al.  Combining approaches for early diagnosis of breast diseases using thermal imaging , 2012 .

[8]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[9]  Nooshin Nabizadeh,et al.  Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation , 2014, Expert Syst. Appl..

[10]  Ying Sun,et al.  A novel fuzzy entropy approach to image enhancement and thresholding , 1999, Signal Process..

[11]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[12]  Andrea Baraldi,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Swagatam Das,et al.  A Differential Evolution Based Approach for Multilevel Image Segmentation Using Minimum Cross Entropy Thresholding , 2011, SEMCCO.

[14]  Yujin Zhang Chapter I An Overview of Image and Video Segmentation in the Last 40 Years , 2006 .

[15]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[16]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[18]  Hong Li,et al.  Evaluation of signal detection performance with pseudocolor display and lumpy backgrounds , 1997, Medical Imaging.

[19]  B. Wills,et al.  Management of Dengue , 2008 .

[20]  Sanguklee,et al.  A comparative performance study of several global thresholding techniques for segmentation , 1990 .

[21]  Syed Abdul Rahman Abu-Bakar,et al.  Adaptive Thresholding Based on Co-occurrence Matrix Edge Information , 2007, Asia International Conference on Modelling and Simulation.

[22]  Vijay Kumar Garg,et al.  Soft computing technique based on ANFIS for the early detection of sleep disorders , 2015, 2015 International Conference on Advances in Computer Engineering and Applications.

[23]  D. Gubler,et al.  Diagnosis of measles by clinical case definition in dengue-endemic areas: implications for measles surveillance and control. , 1992, Bulletin of the World Health Organization.

[24]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[25]  Ashish Kumar Bhandari,et al.  Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy , 2014, Expert Syst. Appl..

[26]  Pragati Kapoor,et al.  Image processing for early diagnosis of breast cancer using infrared images , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[27]  Yi Liu,et al.  Modified particle swarm optimization-based multilevel thresholding for image segmentation , 2014, Soft Computing.

[28]  Yudong Zhang,et al.  Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach , 2011, Entropy.

[29]  Ming-Huwi Horng,et al.  A multilevel image thresholding using the honey bee mating optimization , 2010, Appl. Math. Comput..

[30]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[31]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[32]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[33]  M. Reis,et al.  Referral pattern of leptospirosis cases during a large urban epidemic of dengue. , 2001, The American journal of tropical medicine and hygiene.

[34]  Peng-Yeng Yin,et al.  A fast scheme for optimal thresholding using genetic algorithms , 1999, Signal Process..

[35]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[36]  Ashish Kumar Bhandari,et al.  Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions , 2015, Expert Syst. Appl..

[37]  M. Etehadtavakol,et al.  Level set method for segmentation of infrared breast thermograms , 2014, EXCLI journal.

[38]  P. D. Thouin,et al.  Survey and comparative analysis of entropy and relative entropy thresholding techniques , 2006 .

[39]  Yunjie Zhang,et al.  The Global Fuzzy C-Means Clustering Algorithm , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[40]  B. Chanda,et al.  A note on the use of graylevel co-occurence matrix in threshold selection , 1988 .

[41]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[42]  Trai-Ming Yeh,et al.  Volume replacement in infants with dengue hemorrhagic fever/dengue shock syndrome. , 2006, The American journal of tropical medicine and hygiene.

[43]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[44]  Amanpreet Kaur Comparison between YCbCr Color Space and CIELab Color Space for Skin Color Segmentation , 2012 .

[45]  R. Kayalvizhi,et al.  Optimal multilevel thresholding using bacterial foraging algorithm , 2011, Expert Syst. Appl..

[46]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[47]  Nicholas J White,et al.  Comparison of three fluid solutions for resuscitation in dengue shock syndrome. , 2005, The New England journal of medicine.

[48]  Mahnaz Etehadtavakol,et al.  BREAST THERMOGRAPHY AS A POTENTIAL NON-CONTACT METHOD IN THE EARLY DETECTION OF CANCER: A REVIEW , 2013 .

[49]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[50]  Qiang Chen,et al.  Robust spatially constrained fuzzy c-means algorithm for brain MR image segmentation , 2014, Pattern Recognit..

[51]  J. Farrar,et al.  Acute management of dengue shock syndrome: a randomized double-blind comparison of 4 intravenous fluid regimens in the first hour. , 2001, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[52]  Mita Nasipuri,et al.  An automated system for segmenting platelets from microscopic images of blood cells , 2015, 2015 International Symposium on Advanced Computing and Communication (ISACC).

[53]  F. Albregtsen Statistical Texture Measures Computed from Gray Level Coocurrence Matrices , 2008 .

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

[55]  Sheli Sinha Chaudhuri,et al.  A Differential Evolutionary Multilevel Segmentation of Near Infra-Red Images Using Renyi’s Entropy , 2013 .

[56]  Souradeep Dutta,et al.  Comparative Analysis of Cuckoo Search Optimization-Based Multilevel Image Thresholding , 2015 .

[57]  R. Haralick Image segmentation survey , 1982 .

[58]  Shu-Kai S. Fan,et al.  Optimal multi-thresholding using a hybrid optimization approach , 2005, Pattern Recognit. Lett..

[59]  Zhang Jin-Yu,et al.  IR Thermal Image Segmentation Based on Enhanced Genetic Algorithms and Two-Dimensional Classes Square Error , 2009, 2009 Second International Conference on Information and Computing Science.

[60]  Hairong Qi,et al.  Detecting Breast Cancer from Thermal Infrared Images by Asymmetry Analysis , 2003 .

[61]  M. Frize,et al.  Analysis of breast thermography with an artificial neural network , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[62]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[63]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[64]  Ahmad Ayatollahi,et al.  An efficient neural network based method for medical image segmentation , 2014, Comput. Biol. Medicine.

[65]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[66]  B. Bain,et al.  Dacie and Lewis Practical Haematology , 2006 .

[67]  Gerald Schaefer,et al.  A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification , 2015, Artif. Intell. Medicine.