References
- [1] Priyanka, et al. A review on brain tumor detection using segmentation. International Journal of Computer Science and Mobile Computing. 2013;2(7):48-54.
- [2] Maiti I, Chakraborty M. A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model. in Computing and Communication Systems (NCCCS), 2012 National Conference on. 2012.
- [3] Joshi A, et al. An Efficient Tumor Extraction Algorithm using Segmentation of Multimodal Medical Images.
- [4] Yu Z, et al. Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. Int J Comput Assist Radiol Surg. 2016;11(11):2007-2019.
- [5] Varsha YS, Shyry SP. A Novel Approach for Identifying the Stages of Brain Tumor. International Journal of Computer Trends and Technology (IJCTT) 2014. 10.
- [6] Jazayeri SB, et al. Epidemiology of Primary CNS Tumors in Iran: A Systematic. Asian Pacific Journal of Cancer Prevention. 2013;14(6):3979-3985.10.7314/APJCP.2013.14.6.3979
- [7] Ferlay J, et al. World cancer statistics for the most common cancers 2012. 2012 [cited 2015 16/01/2015.]; Available from: http://www.wcrf.org/int/cancer-facts-figures/worldwide-data.
- [8] Gavrilovic IT, Posner JB. Brain metastases: epidemiology and pathophysiology. Journal of neuro-oncology. 2005;75(1):5-14.
- [9] Damodharan S, Raghavan D. Combining tissue segmentation and neural network for brain tumor detection. Int. Arab J. Inf. Technol. 2015;12(1): 42-52.
- [10] Mehndiratta A, et al. An Introduction to Brain Tumor Imaging, in Tumors of the Central Nervous System, Volume 11. 2014, Springer. p. 3-20.
- [11] El-Melegy MT, Mokhtar HM. Tumor segmentation in brain MRI using a fuzzy approach with class center priors. EURASIP Journal on Image and Video Processing, 2014. 2014(1):1-14.
- [12] Amsaveni V, Singh NA. Detection of brain tumor using neural network. in Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on. 2013.
- [13] Schmidt M, et al. Segmenting brain tumors using alignment-based features. in Machine Learning and Applications, 2005. Proceedings. Fourth International Conference on. 2005.
- [14] Prajapati SJ, Jadhav KR. Brain tumor detection by various image segmentation techniques with introduction to non negative matrix factorization. Brain. 2015;4(3):600-603.
- [15] Al-Ashwal RH, et al. Digital Processing for Computed Tomography Images: Brain Tumor Extraction and Histogram Analysis. in Math Comput Contemp Sci. 14th International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (MMACTEE13). 2012.
- [16] M Prastawa, et al. A brain tumor segmentation framework based on outlier detection. Med Image Anal. 2004;8(3):275-283.10.1016/j.media.2004.06.007
- [17] Ding Z, Wang C, Wang P. Giant Benthic HD Image Feature Extraction and Size Estimation Based on Canny Algorithm. in Proceedings of the 2015 Chinese Intelligent Automation Conference. 2015. Springer.
- [18] Wang Y, Ma S. Automatic detection and segmentation of brain tumor using fuzzy classification and deformable models. in Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on. 2011.
- [19] Heiss WD, et al. Multimodality Assessment of Brain Tumors and Tumor Recurrence. J Nucl Med. 2011;52(10):1585-1600.
- [20] Moon N, et al. Automatic Brain and Tumor Segmentation. MICCAI proceedings, 2002: p. 372-379.
- [21] Wang G, Wang D. Segmentation of Brain MRI Image with GVF Snake Model. in Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on. 2010.
- [22] Bara S, et al. A robust approach for the detection of brain tumors by variational b-spline level-set method and brain extraction. in Multimedia Computing and Systems (ICMCS), 2014 International Conference on. 2014.
- [23] Logeswari T, Karnan M. An improved implementation of brain tumor detection using segmentation based on soft computing. Journal of Cancer Research and Experimental Oncology. 2009;2(1)::006-014.
- [24] Toga AW, et al. Probabilistic approaches for atlasing normal and disease-specific brain variability. Anatomy and embryology. 2001; 204(4):267-282.
- [25] Shanthi U, et al. An Automated Computer Aided System for Tumor Detection in Brain. International Journal of Innovative Research in Computer and Communication Engineering. 2015;3(2):84-88.
- [26] Kumar M, Mehta KK. A Texture based Tumor detection and automatic Segmentation using Seeded Region Growing Method. ijta, 2015;2(4):855-859.
- [27] Fazli S, Nadirkhanlou P. A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images. arxiv, 2013.
- [28] Al-Tamimi MSH, Sulong G. Tumor brain detection through MR images: a review of literature. Journal of Theoretical and Applied Information Technology. 2014;6(2):387-403.
- [29] Malviya AM, Joshi AS. Gabor Wavelet Approach for Automatic Brain Tumor Detection. International Journal of Emerging Technology and Advanced Engineering. 2014;4(4): 826-831.
- [30] Goswami S, Bhaiya LKP. A hybrid neuro-fuzzy approach for brain abnormality detection using GLCM based feature extraction. in Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), 2013 International Conference on. 2013.
- [31] Ibrahim WH, Osman AAA, Mohamed YI. MRI brain image classification using neural networks. in Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on. 2013.
- [32] Phooi Yee L, Voon FCT, Ozawa S. The detection and visualization of brain tumors on T2-weighted MRI images using multiparameter feature blocks. in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the. 2005.
- [33] Havaei M, et al. Brain tumor segmentation with deep neural networks. Medical image analysis. 2017;35:18-31.
- [34] Sachdeva J, et al. A novel content-based active contour model for brain tumor segmentation. Magnetic resonance imaging. 2012;30(5):694-715.
- [35] Aswathy SU, Dhas GGD, Kumar SS. A survey on detection of brain tumor from MRI brain images. in 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). 2014.
- [36] Xuan X, Liao Q. Statistical structure analysis in MRI brain tumor segmentation. in Image and Graphics, 2007. ICIG 2007. Fourth International Conference on. 2007. IEEE.
- [37] Chen D. Interactive Brain Tumor Segmentation with Inclusion Constraints. 2016, The University of Western Ontario.
- [38] Upadhyay N, Waldman A. Conventional MRI evaluation of gliomas. Br J Radiol. 2011;84(Spec No 2):S107-11.10.1259/bjr/65711810
- [39] Sheikh A, Krishna R, Dutt S. Energy Efficient Approach for Segmentation of Brain tumor Using Ant Colony Optimizationǁ. International Journal of Computer Technology and Electronics Engineering (IJCTEE) Volume. 1.
- [40] Dass R, Devi S. Image segmentation techniques 1. 2012.
- [41] Kumar D. Review on different Techniques of Image Segmentation using MATLAB. 2017.
- [42] Chin-Wei B, Rajeswari M. Multiobjective Optimization Approaches in Image Segmentation–The Directions and Challenges. Int. J. Advance. Soft Comput. Appl, 2010. 2(1).
- [43] Arbeláez PA, Cohen LD. Energy partitions and image segmentation. Journal of Mathematical Imaging and Vision. 2004;20(1):43-57.10.1023/B:JMIV.0000011318.77653.44
- [44] Costin H. Recent trends in medical image processing. Computer Science. 2014;22(2):65.
- [45] Withey D, Koles Z. A review of medical image segmentation: methods and available software. International Journal of Bioelectromagnetism. 2008;10(3):125-148.
- [46] Sachin N, et al. Brain Tumor Detection Based On Bilateral Symmetry Information. Int. Journal of Engineering Research and Application. 2013;3(6):430-432.
- [47] Constantin AA, Bajcsy BR, Nelson CS. Unsupervised segmentation of brain tissue in multivariate MRI. in Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. 2010.
- [48] Kobashi S, et al. Adaptive Brain Tissue Classification with Fuzzy Spatial Modeling in 3T IR-FSPGR MR Images. in Automation Congress, 2006. WAC '06. World. 2006.
- [49] Yu CP, et al. 3D blob based brain tumor detection and segmentation in MR images. in Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on. 2014.
- [50] D Jude hemanth, Selvathi D, Anitha J. Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation. IEEE International Advance Computing Conference. 2009;6(7):609-614.
- [51] Karnan M, Selvanayaki K. Improved implementation of brain MR image segmentation using Meta heuristic algorithms. in Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on. 2010.
- [52] Idrissi N, Ajmi FE. A hybrid segmentation approach for brain tumor extraction and detection. in Multimedia Computing and Systems (ICMCS), 2014 International Conference on. 2014.
- [53] Charutha S, Jayashree MJ. An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection. in Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on. 2014.
- [54] Vijay J, Subhashini J. An efficient brain tumor detection methodology using K-means clustering algoriftnn. in Communications and Signal Processing (ICCSP), 2013 International Conference on. 2013.
- [55] Mustaqeem A, Javed A, Fatima T. An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. International Journal of Image, Graphics and Signal Processing. 2012;4(10):34.
- [56] Cuadra MB, et al. Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE transactions on medical imaging. 2004;23(10):1301-1314.
- [57] Shen S, et al. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE transactions on information technology in biomedicine. 2005;9(3):459-467.
- [58] Corso JJ, et al. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE transactions on medical imaging. 2008;27(5):629-640.
- [59] Hall LO, et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE transactions on neural networks. 1992;3(5):672-682.
- [60] Bauer S, Nolte L-P, Reyes M. Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. Medical image computing and computer-assisted intervention–MICCAI 2011, 2011: p. 354-361.
- [61] Aslan Ö, et al. Convex two-layer modeling. in Advances in Neural Information Processing Systems. 2013.
- [62] Cho Y, Saul LK. Kernel methods for deep learning. in Advances in neural information processing systems. 2009.
- [63] Deng L, et al. Use of kernel deep convex networks and end-to-end learning for spoken language understanding. in Spoken Language Technology Workshop (SLT), 2012 IEEE. 2012. IEEE.
- [64] Vinyals O, et al. Learning with recursive perceptual representations. in Advances in Neural Information Processing Systems. 2012.
- [65] Huertas-Company M, et al. A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning. arXiv preprint arXiv:1509.05429, 2015.
- [66] Bauer S, et al. Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and nonrigid registration. in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE. 2010. IEEE.
- [67] Pereira S, et al. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging. 2016;35(5):1240-1251.
- [68] Baker JM, et al. Developments and directions in speech recognition and understanding, Part 1 [DSP Education]. IEEE Signal Processing Magazine. 2009;26(3):75-80.
- [69] Baker J. et al. Updated MINDS report on speech recognition and understanding, Part 2. 2009. Institute of Electrical and Electronics Engineers.
- [70] Deng L. Computational models for speech production, in Computational models of speech pattern processing. 1999, Springer. p. 199-213.
- [71] Deng L. Switching dynamic system models for speech articulation and acoustics, in Mathematical Foundations of Speech and Language Processing. 2004, Springer. p. 115-133.
- [72] George, D., How the brain might work: A hierarchical and temporal model for learning and recognition. 2008, Citeseer.
- [73] Bouvrie JV. Hierarchical learning: Theory with applications in speech and vision. 2009, Citeseer.
- [74] Poggio T. How the brain might work: The role of information and learning in understanding and replicating intelligence. Information: Science and Technology for the New Century, 2007: 45-61.
- [75] Deng L, Yu D. Deep Learning. Signal Processing. 2014;7:3-4.
- [76] Hinton GE, Osindero S, The Y-W. A fast learning algorithm for deep belief nets. Neural computation. 2006;18(7):1527-1554.
- [77] Mohamed A-r, Hinton GE. Phone recognition using Restricted Boltzmann Machines. in ICASSP. 2010.
- [78] Le QV, et al. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. 2011. IEEE.
- [79] Vincent P, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research. 2010;11: 3371-3408.
- [80] Zhou X, et al. Product Image Search with Deep Attribute Mining and Re-ranking. in Pacific Rim Conference on Multimedia. 2016. Springer.
- [81] Bengio Y. Learning deep architectures for AI. Foundations and trends® in Machine Learning. 2009;2(1):1-127.
- [82] Ciresan D, et al. Deep neural networks segment neuronal membranes in electron microscopy images. in Advances in neural information processing systems. 2012.
- [83] Vo DM, Le TH. Deep generic features and SVM for facial expression recognition. in Information and Computer Science (NICS), 2016 3rd National Foundation for Science and Technology Development Conference on. 2016. IEEE.
- [84] LeCun Y, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 86(11):2278-2324.10.1109/5.726791
- [85] Huang FJ, LeCun Y. Large-scale learning with svm and convolutional for generic object categorization. in Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. 2006. IEEE.
- [86] Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016.
- [87] Waibel A, et al. Phoneme recognition using time-delay neural networks. Acoustics, Speech and Signal Processing, IEEE Transactions on, 1989:37(3): 328-339.
- [88] Davy A, et al. Brain tumor segmentation with deep neural networks. 2014.
- [89] Zikic D, et al. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS, 2014:36-39.
- [90] Agn M, et al. Brain tumor segmentation by a generative model with a prior on tumor shape. Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, 2015: 1-4.
- [91] Dvorak P. Menze B. Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, 2015:13-24.
- [92] Rao V. Shari Sarabi M, Jaiswal. Brain tumor segmentation with deep learning. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), 2015: 56-59.
- [93] Lyksborg M, et al. An ensemble of 2D convolutional neural networks for tumor segmentation. in Scandinavian Conference on Image Analysis. 2015. Springer.
- [94] Rewari R. Automatic Tumor Segmentation from MRI scans. Stanford University.
- [95] Pan Y, et al. Brain tumor grading based on neural networks and convolutional neural networks. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015. IEEE.
- [96] Zhao L, Jia K. Deep feature learning with discrimination mechanism for brain tumor segmentation and diagnosis. in 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). 2015. IEEE.
- [97] Havaei M, et al. Brain tumor segmentation with deep neural networks. Medical Image Analysis, 2016.
- [98] Ghafoorian M., et al. Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. arXiv preprint arXiv:1610.04834, 2016.
- [99] Chen H, et al. VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation. arXiv preprint arXiv:1608.05895, 2016.
- [100] Yi D. et al. 3-D Convolutional Neural Networks for Glioblastoma Segmentation. arXiv preprint arXiv:1611.04534, 2016.
- [101] Chang PD. Fully Convolutional Deep Residual Neural Networks for Brain Tumor Segmentation, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers, A. Crimi, et al., Editors. 2016, Springer International Publishing: Cham. p. 108-118.
- [102] Kamnitsas K, et al. DeepMedic on Brain Tumor Segmentation. Athens, Greece Proc. BRATS-MICCAI, 2016.
- [103] Casamitjana A, et al. 3D convolutional networks for brain tumor segmentation. Proceedings of the MICCAI Challenge on Multimodal Brain Tumor Image Segmentation (BRATS), 2016:65-68.
- [104] Zhao X, et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. arXiv preprint arXiv:1702.04528, 2017.
- [105] Lun T, Hsu W. Brain tumor segmentation using deep convolutional neural network. Proceedings of BRATS-MICCAI, 2016.
- [106] MICCAI. about MICCAI. 2016 [cited 2016; Available from: http://www.miccai.org/organization].
- [107] Veta M, et al. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PloS one. 2013;8(7): e70221.
- [108] Kang J. et al. Neuron sparseness versus connection sparseness in deep neural network for large vocabulary speech recognition. in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2015. IEEE.
- [109] Kleć M, Koržinek D. Pre-trained deep neural network using sparse autoencoders and scattering wavelet transform for musical genre recognition. Computer Science. 2015;16(2): 133-144.
- [110] Oyallon E, Mallat S, Sifre L. Generic deep networks with wavelet scattering. arXiv preprint arXiv:1312.5940, 2013.
- [111] Hassairi S, Ejbali R, Zaied M. Supervised image classification using Deep Convolutional Wavelets Network. in Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on. 2015. IEEE.