Brain Tumors Detection on MRI Images through Extracting HOG Features

seyed Enayatallah Alavi, Ehsan zare, Mohammad javad Rashti

Abstract


Abstract: In this paper, a system of Computer Aided Diagnosis (CAD) is introduced for detecting brain tumors through MRI images. The proposed system consists of three main sections: segmentation, feature extraction, and categorization. In the segmentation phase, a system called Seeded Region Growing (SRG) is applied to separate brain tissue from other regions. In the feature extraction phase, the Histograms of Oriented Gradients (HOG) algorithm is used. Finally, in order to describe the images of the brain, we use Support Vector Machine (SVM) and classify the images in two tumor-free and tumor-grade groups, and then compare them with similar tasks. The results show a high efficiency of this approach compared to other methods. 850 MRI images have been applied to test and teach samples; the number of healthy brains is 300 and the number of defective brains is 550, and the system has achieved a precision rate of 93.2% in the categorization.  


Keywords


Tumor detection, Histograms of Oriented Gradients, Feature extraction, Support Vector Machine

References


Mehdi Jafari, Shohreh Kasaei. Automatic BrainTissue Detection in MRI Images Using Seeded Region Growing Segmentation and Neural Network Classification. Journal of Basic and Applied Sciences 2011, Australia.

AmirEhsan Lashkari. A Neural Network based Method for Brain Abnormality Detection in MR Images Using Gabor Wavelets, International Journal of Computer Applications, July 2011, Iran.

Ramathilagam, S.Pandiyarajan, R.Sathya, A.Devi, R.Kannan, Modified fuzzy cmeans algorithm for segmentation of T1--T2-weighted brain MRI, Journal of Computational and Applied Mathematics, Elsevier, 2011. pp. 1578-1586.

Perona,P, Malik,J. Scale-space and edge detection using anisotropic diffusion, Pattrrn Analysis and Machine Intelligence ,IEEE Transactions, 1990.pp. 629-639.

P. Puranik, P. Baja, A. Abraham, P. Palsodkar, A. Deshmukh. Human Perception-based Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization, journal of information hiding and multimedia signal processing, July 2011, ISSN 4212-2073, Vol. 2, pp. 227-235.

Mehdi Jafari, Shohreh Kasaei . Automatic Brain Tissue Detection in MRI Images Using Seeded Region Growing Segmentation and Neural Network Classification, Journal of Basic and Applied Sciences 2011, Australia.

Matthew C. Clark, Lawrence O. Hall, Robert Velthuizen. Automatic Tumor Segmentation Using Knowledge - Based Techniques, IEEE Transactions On Medical Imaging , April 1998, USA.

El-Sayed Ahmed El - Dahshan,Tamer Hosny, Abdel -Badeeh M. Salem, Hybrid intelligent techniques for MRI brain images classification, Elsevier, Digital Signal Processing, 2010, pp. 433-441.

Amir Ehsan Lashkari. A Neural Network based Method for Brain Abnormality Detection in MR Images Using Gabor Wavelets, International Journal of Computer Applications July 2010,Vol.4 –No.7.

D.Jude hemanthl,D.Selvathi,J.Anitha. Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation,IEEE International Advance Computing Conference 2009,India.

Phooi Yee Lau, Frank C.T.Voon, Shinji Ozawa. The detection and visualization of brain tumors on T2- weighted MRI images using multi parameter feature blocks,Proceedings of the 2005 IEEE Engineering in Medicine.

Mamata S.Kalas. An Artificial Neural Network for Detection of Biological Early Brain Cancer, International Journal of Computer Applications 2010, Vol.1 – No. 6.

Song-yun Xie,Rang Guo,Ning - fei Li,Ge Wang,Hai - tao Zhao. Brain MRI Processing and Classification Based on Combination of PCA and SVM , Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, June 2009, USA.

Jafari, M. Kasaei. Automatic Brain Tissue Detection in MRI Images Using Seeded Region Growing Segmentation and Neural Network Classification, Australian Journal of Basic and Applied Sciences, 2011.pp.1066-1079.

Gaurav Kumar,Pradeep Kumar Bhatia. A Detalid Review of Feature Extraction in Image Processing Systems, Advanced Computing & Communication Technologies ACCT 2014, pp.5-12.

Dalal.N, Triggs.B. Histograms of oriented gradients for human detection, In CVPR 2005, vol.I,pp.886-893.

Netter, Frank. Atlas of Human Anatomy Including Student Consult Interactive Ancillaries and Guides, Penn, Philadelphia,2014.114p.

J.Vijay and J.Subhashini. An Efficient Brain Tumor Detection Methodology Using K- Means Clustering Algorithm, in International conference on Communication and Signal Processing, April 2013, 3-5, pp.653-658.

Minakshi Sharma, Dr. Sourabh Mukharjee. Brain Tumor Segmentation using hybrid Genetic Algorithm and Artificial Neural Network Fuzzy Inference System, International Journal of Fuzzy Logic Systems (IJFLS) October 2012. Vol.2, No.4.

V. Palanisamy R. Karuppathal, FUZZY BASED AUTOMATIC DETECTION AND CLASSIFICATION APPROACH FOR MRI-BRAIN TUMOR, ARPN Journal of Engineering and Applied Sciences DECEMBER 2014, ISSN 1819-6608.

Robert P.Velthuizen, Lawrence Hallt, Laurence P.Clarke. Mri Feature Extraction Using Genetic Algorithms ,18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1996, Amsterdam.

Brin Tumor MRI T2 are available To http://www.ics.uci.edu/ml/datasets.html.

Perona,P. and Malik.J. Scale-space and edge detection using anisotropic diffusion, Pattrrn Analysis and Machine Intelligence ,IEEE Transactions 1990,Vol.12, No.7,pp. 629-6.


Refbacks





Copyright (c) 2018 seyed enatallah Alavi, Ehsan zare, Mohammad javad Rashti

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.