Brain tumor segmentation using DE embedded OTSU method and neural network

In the past few decades, medical imaging and soft computing have shown a symbolic growth in brain tumor segmentation. Research in medical imaging is becoming quite popular field, particularly in magnetic resonance images of brain tumor, because of the tremendous need of efficient and effective technique for evaluation of large amount of data. Image segmentation is considered as one of the most crucial techniques for visualizing tissues in human being. In considering brain tumor image segmentation, manually with an expert, it is more likely that the errors are present in it. To automate image segmentation, we have proposed an algorithm to obtain a global thresholding value for a particular image. To find out an optimal threshold value we have used Differential Evolution algorithm embedded with OTSU method and trained neural network for future use. Proposed Methodology provides classification of the images successfully for brain tumors. Results show its efficiency over other methods.

[1]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[2]  Noppadol Chumchob,et al.  International Journal of C 2009 Institute for Scientific Numerical Analysis and Modeling Computing and Information a Robust Affine Image Registration Method , 2022 .

[3]  P. Lauterbur,et al.  Image Formation by Induced Local Interactions: Examples Employing Nuclear Magnetic Resonance , 1973, Nature.

[4]  Ayça Çakmak Pehlivanli,et al.  PCA based clustering for brain tumor segmentation of T1w MRI images , 2017, Comput. Methods Programs Biomed..

[5]  J Toriwaki,et al.  Automatic segmentation of head MRI images by knowledge guided thresholding. , 1991, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[6]  Joachim Weickert,et al.  Anisotropic diffusion in image processing , 1996 .

[7]  Arthur W. Toga,et al.  Skull-stripping magnetic resonance brain images using a model-based level set , 2006, NeuroImage.

[8]  W. W. Hansen,et al.  The Nuclear Induction Experiment , 1946 .

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Yu Xue,et al.  Research on denoising sparse autoencoder , 2016, International Journal of Machine Learning and Cybernetics.

[11]  Paul Suetens.  Fundamentals of Medical Imaging , 2002 .

[12]  R. Kayalvizhi,et al.  Optimal segmentation of brain MRI based on adaptive bacterial foraging algorithm , 2011, Neurocomputing.

[13]  Christos P. Loizou,et al.  Brain White Matter Lesions Classification in Multiple Sclerosis Subjects for the Prognosis of Future Disability , 2011, EANN/AIAI.

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

[15]  C. Pattichis,et al.  Brain MR image normalization in texture analysis of multiple sclerosis , 2009 .

[16]  G. Hagemann,et al.  Fast, accurate, and reproducible automatic segmentation of the brain in T1‐weighted volume MRI data , 1999, Magnetic resonance in medicine.

[17]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[18]  Peng-Yeng Yin,et al.  Multilevel minimum cross entropy threshold selection based on particle swarm optimization , 2007, Appl. Math. Comput..

[19]  C. Pattichis,et al.  Brain MR image normalization in texture analysis of multiple sclerosis , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[20]  D. Langleben,et al.  PET in differentiation of recurrent brain tumor from radiation injury. , 2000, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[21]  Kit Po Wong,et al.  Differential Evolution, an Alternative Approach to Evolutionary Algorithm , 2006, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[22]  Kenneth V. Price,et al.  An introduction to differential evolution , 1999 .

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

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

[25]  S. Bauer,et al.  A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.

[26]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[27]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[28]  S Petroudi,et al.  Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. , 2015, Journal of neuroradiology. Journal de neuroradiologie.

[29]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[30]  Wenyuan Xu,et al.  Behavioral analysis of anisotropic diffusion in image processing , 1996, IEEE Trans. Image Process..

[31]  Jianfeng Lu,et al.  A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation , 2007, Computing in Science & Engineering.

[32]  Nickolas Papanikolaou,et al.  Imaging Modalities in Brain Tumors , 2011 .

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

[34]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[35]  Victor Alves,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.

[36]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[37]  Marios S. Pattichis,et al.  Multiscale Amplitude-Modulation Frequency-Modulation (AM–FM) Texture Analysis of Multiple Sclerosis in Brain MRI Images , 2011, IEEE Transactions on Information Technology in Biomedicine.

[38]  Paul Suetens,et al.  Comprar Fundamentals of Medical Imaging | Paul Suetens | 9780521519151 | Cambridge University Press , 2009 .

[39]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[40]  Xin Yu,et al.  Automatic threshold selection based on ant colony optimization algorithm , 2005, 2005 International Conference on Neural Networks and Brain.

[41]  Eduard Gröller,et al.  3D watershed transform combined with a probabilistic atlas for medical image segmentation , 2003 .

[42]  Jue Wu,et al.  A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model , 2007, IEEE Transactions on Image Processing.

[43]  J. Barnholtz-Sloan,et al.  CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. , 2012, Neuro-oncology.

[44]  Chulhee Lee,et al.  Skull stripping based on region growing for magnetic resonance brain images , 2009, NeuroImage.

[45]  Ron Kikinis,et al.  Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.

[46]  P. Sathyanarayana,et al.  Image Texture Feature Extraction Using GLCM Approach , 2013 .

[47]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[48]  H. Hannah Inbarani,et al.  Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification , 2016, Appl. Soft Comput..

[49]  Ashish Ghosh,et al.  Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation. , 2016, Magnetic resonance imaging.

[50]  Zhongzhi Shi,et al.  Denoising Laplacian multi-layer extreme learning machine , 2016, Neurocomputing.

[51]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[52]  Neeraj Sharma,et al.  Automated medical image segmentation techniques , 2010, Journal of medical physics.

[53]  Ville Tirronen,et al.  Scale factor local search in differential evolution , 2009, Memetic Comput..

[54]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[55]  A. R. Kavitha,et al.  Automated Brain Tumor Segmentation and Detection in MRI Using Enhanced Darwinian Particle Swarm Optimization(EDPSO) , 2016 .

[56]  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.

[57]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[58]  Sushil Kumar,et al.  Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms , 2015, International Journal of Machine Learning and Cybernetics.

[59]  Inan Güler,et al.  Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks , 2015, IEEE Journal of Biomedical and Health Informatics.

[60]  Sushil Kumar,et al.  Bi-level thresholding using PSO, Artificial Bee Colony and MRLDE embedded with Otsu method , 2013, Memetic Comput..

[61]  B. Scheithauer,et al.  The New WHO Classification of Brain Tumours , 1993, Brain pathology.

[62]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[63]  James Nga-Kwok Liu,et al.  Recent advances of statistics in computational intelligence (RASCI) , 2018, Int. J. Mach. Learn. Cybern..

[64]  Jayaram K. Udupa,et al.  New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.

[65]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

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

[67]  Sushil Kumar,et al.  Differential evolution embedded Otsu's method for optimized image thresholding , 2011, 2011 World Congress on Information and Communication Technologies.

[68]  Paul Suetens,et al.  Fundamentals of Medical Imaging by Paul Suetens , 2009 .