Brain Tumor Segmentation Using OTSU Embedded Adaptive Particle Swarm Optimization Method and Convolutional Neural Network

Medical imaging and deep learning have tremendously shown improvement in research field of brain tumor segmentation. Data visualization and exploration plays important role in developing robust computer aided diagnosis system. The analysis is performed in proposed work to provide automation in brain tumor segmentation. The adaptive particle swarm optimization along with OTSU is contributed to determine the optimal threshold value. Anisotropic diffusion filtering is applied on brain MRI to remove the noise and improving the image quality. The extracted features provided as data for training the convolutional neural network and performing classification. The proposed research achieves higher accuracy of 98% which is better over existing methods.

[1]  H. Greenspan,et al.  Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. , 2019, Radiology.

[2]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[3]  Sathya P. Duraisamy,et al.  A New Multilevel Thresholding Method Using Swarm Intelligence Algorithm for Image Segmentation , 2010, J. Intell. Learn. Syst. Appl..

[4]  Hao Chen,et al.  VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images , 2017, NeuroImage.

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

[6]  Abdel-Badeeh M. Salem,et al.  Hybrid intelligent techniques for MRI brain images classification , 2010, Digit. Signal Process..

[7]  Jian Guo,et al.  A Quick Artificial Bee Colony Algorithm for Image Thresholding , 2017, Inf..

[8]  Govind Narayan Purohit,et al.  Information Retrieves from Brain MRI Images for Tumor Detection Using Hybrid Technique K-means and Artificial Neural Network (KMANN) , 2018 .

[9]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[10]  Vinod Kumar,et al.  Classification of brain tumors using PCA-ANN , 2011, 2011 World Congress on Information and Communication Technologies.

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

[12]  Kaliyil Janardhan Shanthi,et al.  Neuro-Fuzzy Approach Toward Segmentation of Brain MRI Based on Intensity and Spatial Distribution. , 2010, Journal of medical imaging and radiation sciences.

[13]  Nilanjan Dey,et al.  Multilevel Threshold Based Gray Scale Image Segmentation using Cuckoo Search , 2013, ArXiv.

[14]  Matthew J. McAuliffe,et al.  Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

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

[16]  P. Mahzouni,et al.  Brain tumors: Special characters for research and banking , 2015, Advanced biomedical research.

[17]  Kebin Jia,et al.  Deep Feature Learning with Discrimination Mechanism for Brain Tumor Segmentation and Diagnosis , 2015, 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP).

[18]  J H Goodband,et al.  A comparison of neural network approaches for on-line prediction in IGRT. , 2008, Medical physics.

[19]  Kenji Suzuki,et al.  Overview of deep learning in medical imaging , 2017, Radiological Physics and Technology.

[20]  V. Rajinikanth,et al.  Image Multithresholding based on Kapur/Tsallis Entropy and Firefly Algorithm , 2016 .

[21]  Yong Fan,et al.  A deep learning model integrating FCNNs and CRFs for brain tumor segmentation , 2017, Medical Image Anal..

[22]  J Jiang,et al.  Medical image analysis with artificial neural networks , 2010, Comput. Medical Imaging Graph..

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

[24]  M. Kohli,et al.  Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment. , 2019, AJR. American journal of roentgenology.

[25]  V. Soleimani,et al.  Improving ant colony optimization for brain MRI image segmentation and brain tumor diagnosis , 2013, 2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA).

[26]  Geethu Mohan,et al.  MRI based medical image analysis: Survey on brain tumor grade classification , 2018, Biomed. Signal Process. Control..

[27]  Nahla Ben Amor,et al.  Brain Tumor Segmentation Using Support Vector Machines , 2009, ECSQARU.

[28]  Huai Li,et al.  Artificial convolution neural network for medical image pattern recognition , 1995, Neural Networks.

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

[30]  Huai Li,et al.  Optimization of wavelet decomposition for image compression and feature preservation , 2003, IEEE Transactions on Medical Imaging.

[31]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[32]  Parashkev Nachev,et al.  Computer Methods and Programs in Biomedicine NiftyNet: a deep-learning platform for medical imaging , 2022 .

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

[34]  James M. Brown,et al.  CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images , 2019, ArXiv.

[35]  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.

[36]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

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

[38]  Md. Foisal Hossain,et al.  Classification of tumors and it stages in brain MRI using support vector machine and artificial neural network , 2017, 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE).

[39]  Heung-Il Suk,et al.  Deep Learning in Medical Image Analysis. , 2017, Annual review of biomedical engineering.

[40]  Iker Gondra,et al.  MRI segmentation fusion for brain tumor detection , 2017, Inf. Fusion.

[41]  Shailendra Narayan Singh,et al.  Brain tumor segmentation using DE embedded OTSU method and neural network , 2018, Multidimens. Syst. Signal Process..

[42]  Musbah J. Aqel,et al.  Survey on Image Segmentation Techniques , 2015 .

[43]  Guan Xinping.  Fast image segmentation based on particle swarm optimization and two-dimension Otsu method , 2007 .

[44]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[45]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[46]  Yaozong Gao,et al.  Fully convolutional networks for multi-modality isointense infant brain image segmentation , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[47]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

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

[49]  Xiaohong W. Gao,et al.  Classification of CT brain images based on deep learning networks , 2017, Comput. Methods Programs Biomed..

[50]  June-Goo Lee,et al.  Deep Learning in Medical Imaging: General Overview , 2017, Korean journal of radiology.

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

[52]  AmirEhsan Lashkari,et al.  A Neural Network based Method for Brain Abnormality Detection in MR Images Using Gabor Wavelets , 2010 .

[53]  Nidahl K. El Abbadi,et al.  Brain Cancer classification Based on Features and Artificial Neural Network , 2017 .

[54]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[55]  Juan Manuel Górriz,et al.  Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies , 2013, Appl. Soft Comput..

[56]  Sébastien Ourselin,et al.  Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks , 2017, BrainLes@MICCAI.

[57]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[58]  Xiaogang Wang,et al.  Medical image classification with convolutional neural network , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

[59]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[60]  Ernestina Casiraghi,et al.  GLCM, an image analysis technique for early detection of biofilm , 2016 .

[61]  U. Rajendra Acharya,et al.  Application of deep transfer learning for automated brain abnormality classification using MR images , 2019, Cognitive Systems Research.

[62]  Lipo Wang,et al.  Deep Learning Applications in Medical Image Analysis , 2018, IEEE Access.

[63]  Kenji Suzuki,et al.  Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[64]  Nourhan Zayed,et al.  Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities , 2015, Int. J. Biomed. Imaging.

[65]  B. Erickson,et al.  Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

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

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

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