Automated brain tumour segmentation techniques— A review

Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography (PET) image, Computer Tomography (CT) image and Magnetic Resonance Image (MRI). Multimodal imaging techniques (such as PET/CT and PET/MRI) that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 66–77, 2017

[1]  J. Jayakumari,et al.  Modified texture based region growing segmentation of MR brain images , 2013, 2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES.

[2]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[3]  Inan Güler,et al.  Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation , 2011, Eng. Appl. Artif. Intell..

[4]  Kenneth Revett,et al.  Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm , 2014, Expert Syst. Appl..

[5]  Wilfried Philips,et al.  MRI Segmentation of the Human Brain: Challenges, Methods, and Applications , 2015, Comput. Math. Methods Medicine.

[6]  K. Satya Prasad,et al.  Advanced Morphological Technique for Automatic Brain Tumor Detection and Evaluation of Statistical Parameters , 2016 .

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

[8]  Rajaram M Gowda.  Brain Tumour Segmentation Using K-Means And Fuzzy C-Means Clustering Algorithm , 2013 .

[9]  Qiang Chen,et al.  Adaptive scale fuzzy local Gaussian mixture model for brain MR image segmentation , 2014, Neurocomputing.

[10]  J. Buatti,et al.  Globally Optimal Tumor Segmentation in PET-CT Images: A Graph-Based Co-segmentation Method , 2011, IPMI.

[11]  Reyer Zwiggelaar,et al.  Region-based active surface modelling and alpha matting for unsupervised tumour segmentation in PET , 2012, 2012 19th IEEE International Conference on Image Processing.

[12]  Huan Yu,et al.  Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning , 2009, IEEE Transactions on Medical Imaging.

[13]  M. M. Sufyan Beg,et al.  Improved Edge Detection Algorithm for Brain Tumor Segmentation , 2015, Procedia Computer Science.

[14]  Pierre Boulanger,et al.  Atlas to patient registration with brain tumor based on a mesh-free method , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  Claire Chalopin,et al.  Active contours driven by Cuckoo Search strategy for brain tumour images segmentation , 2016, Expert Syst. Appl..

[16]  Jayaram K. Udupa,et al.  Co-segmentation of Functional and Anatomical Images , 2012, MICCAI.

[17]  Kolasani Ramchand H. Rao,et al.  Tumor Detection In Brain Using Genetic Algorithm , 2016 .

[18]  Dongsheng Guo,et al.  CP-activated WASD neuronet approach to Asian population prediction with abundant experimental verification , 2016, Neurocomputing.

[19]  Witold Pedrycz,et al.  Advances in Fuzzy Clustering and its Applications , 2007 .

[20]  Goo-Rak Kwon,et al.  Level set method with automatic selective local statistics for brain tumor segmentation in MR images , 2013, Comput. Medical Imaging Graph..

[21]  Xinjian Chen,et al.  Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images , 2013, Medical Image Anal..

[22]  Shweta Pandav,et al.  Brain Tumor Extraction using Marker Controlled Watershed Segmentation , 2014 .

[23]  Jayaram K. Udupa,et al.  Fuzzy Connectedness Image Co-segmentation for HybridPET/MRI and PET/CT Scans , 2015 .

[24]  Tae-Sun Choi,et al.  Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor , 2014, Appl. Soft Comput..

[25]  Chengan Guo,et al.  An adaptive vector quantization approach for image segmentation based on SOM network , 2015, Neurocomputing.

[26]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[27]  Vinod Kumar,et al.  A novel content-based active contour model for brain tumor segmentation. , 2012, Magnetic resonance imaging.

[28]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[29]  T. Arbel,et al.  Probabilistic Gabor and Markov Random Fields Segmentation of B rain Tumours in MRI Volumes , 2012 .

[30]  Vinod Kumar,et al.  A package-SFERCB-"Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors" , 2016, Appl. Soft Comput..

[31]  Su Ruan,et al.  Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images , 2015, Medical Image Anal..

[32]  D. Louis Collins,et al.  Hierarchical Probabilistic Gabor and MRF Segmentation of Brain Tumours in MRI Volumes , 2013, MICCAI.

[33]  Irina Voiculescu,et al.  An Overview of Current Evaluation Methods Used in Medical Image Segmentation , 2015 .

[34]  Pritee Gupta,et al.  Implementation of Brain Tumor Segmentation in brain MR Images using K-Means Clustering and Fuzzy C-Means Algorithm , 2013, BIOINFORMATICS 2013.

[35]  Fabiano Reis,et al.  Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps , 2015, Journal of the Neurological Sciences.

[36]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[37]  Sobri Muda,et al.  Segmentation of brain lesions in diffusion-weighted MRI using thresholding technique , 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[38]  Habib Zaidi,et al.  PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques , 2010, European Journal of Nuclear Medicine and Molecular Imaging.

[39]  Zohreh Azimifar,et al.  Brain volumetry: An active contour model-based segmentation followed by SVM-based classification , 2011, Comput. Biol. Medicine.

[40]  Aditi Sharan,et al.  An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation , 2016, Appl. Soft Comput..

[41]  Yogita K. Dubey,et al.  Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering , 2016 .

[42]  Loay Kadom Abood,et al.  Segmentation and estimation of brain tumor volume in computed tomography scan images using hidden Markov random field Expectation Maximization algorithm , 2015, 2015 IEEE Student Conference on Research and Development (SCOReD).

[43]  Divya Mathur,et al.  A Novel Approach to Improve Sobel Edge Detector , 2016 .

[44]  J. Suri,et al.  Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology , 2001 .

[45]  S. Bauer,et al.  Atlas-based segmentation of brain tumor images using a Markov Random Field-based tumor growth model and non-rigid registration , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[46]  Reyer Zwiggelaar,et al.  Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting , 2013, Comput. Biol. Medicine.

[47]  R. Sukanesh,et al.  SVM Based Classification of Soft Tissues in Brain CT Images Using Wavelet Based Dominant Gray Level Run Length Texture Features , 2013 .

[48]  Doina Precup,et al.  Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[49]  Yuhong Li,et al.  Brain tumor segmentation from multimodal magnetic resonance images via sparse representation , 2016, Artif. Intell. Medicine.

[50]  Bodo Rosenhahn,et al.  Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images , 2014, Comput. Medical Imaging Graph..

[51]  Torsten Rohlfing,et al.  Quo Vadis, Atlas-Based Segmentation? , 2005 .

[52]  Wen-June Wang,et al.  Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing. , 2012, Magnetic resonance imaging.

[53]  R. Sukanesh A Padma,et al.  Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features , 2011 .

[54]  Abdul Rahman Ramli,et al.  Review of brain MRI image segmentation methods , 2010, Artificial Intelligence Review.

[55]  R. Sukanesh,et al.  TEXTURE FEATURE BASED ANALYSIS OF BRAIN CT IMAGES FOR DISCRIMINATING BENIGN, MALIGNANT TUMORS , 2012 .

[56]  Edward R. Dougherty,et al.  An introduction to morphological image processing , 1992 .