Research Journal of Pharmacy and Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Simple Approach to Automated Brain Tumor Segmentation and Classification


Affiliations
1 Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, India
2 Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, India
3 Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, India
     

   Subscribe/Renew Journal


Brain tumor is the abnormal growth of superfluous cells in central nervous system or brain. It is fact that brain tumor is second most common of cancer death among young people. There are two key categories of brain tumor as cancerous and non cancerous. The cancerous brain tumor is called as Malignant. It spreads very quickly and difficult to remove. The non-cancerous tumor, called Benign, growth rate is very slow as compared to malignant one and easy to remove. The work proposes a simple but more efficient method to detect and segment the brain tumor from the MRI image. The proposed work based on the threshold segmentation for the segmentation of the brain tumor. The MRI image of the brain is taken and processed in such a way so that the tumor is extracted from the given MRI image and displays the segmented part of the image which contains the tumor. The otsu global threshold performs tumor segmentation and image area opening applies to remove the small components form the tumor portion. The gray level co-occurrence matrix and other image quality measures computes (extracts) the features from the segmented image. Support vector machine classifier is finally classifies the tumor, either benign or malignant, based on the extracted features.

Keywords

Brain Tumor, Threshold, Principal Component Analysis, Discrete Wavelet Transform, Gray-Level Co-Occurrence Matrix, Support Vector Machine.
Subscription Login to verify subscription
User
Notifications
Font Size


  • S. Pereira, A. Pinto, V. Alves and C. A. Silva, "Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1240-1251, May 2016.

  • S. Bauer, "A survey of MRI-based medical image analysis for brain tumor studies", Phys. Med. Biol., vol. 58, no. 13, pp. 97-129, 2013.

  • Kalist V, Ganesan P, Sathish BS, and Jenitha JMM. Possiblistic-Fuzzy C-Means Clustering Approach for the Segmentation of Satellite Images in HSL Color Space. Procedia Computer Science. 57; 2015; 49-56.

  • J.J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, A. Yuille, "Efficient multilevel brain tumor segmentation with integrated bayesian model classification", IEEE Transactions on Medical Imaging, vol. 27, no. 5, pp. 629-640, 2008.

  • R. A. Heckemann, J. V. Hajnal, P. Aljabar, D. Rueckert, A. Hammers, "Automatic anatomical brain MRI segmentation combining label propagation and decision fusion", Neuro Image, vol. 33, pp. 115-126, 2006.

  • Shaik KB, Ganesan P, Kalist V, and Sathish BS. Comparative Study of Skin Color Detection and Segmentation in HSV and YCbCr Color Space. Procedia Computer Science. 57; 2015; 41-48.

  • M. Huang, W. Yang, Y. Wu, J. Jiang, W. Chen and Q. Feng, "Brain Tumor Segmentation Based on Local Independent Projection-Based Classification," in IEEE Transactions on Biomedical Engineering, vol. 61, no. 10, pp. 2633-2645, Oct. 2014.

  • S. Bauer, L. P. Nolte, M. Reyes, "Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization", Proc. Med. Image Comput. Comput. Assist. Interv., pp. 354-361, 2011.

  • Ganesan P and Shaik KB. HSV color space based segmentation of region of interest in satellite images. 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). 2014; 101-105.doi: 10.1109/ICCICCT.2014.6992938

  • Sajiv G and Ganesan P. Comparative Study of Possiblistic Fuzzy C-Means Clustering based Image Segmentation in RGB and CIELuv Color Space. International Journal of Pharmacy & Technology. 8(1); 2016; 10899-10909.

  • Malay Bhushan, Niraj Bishwash, and kalist V. Wireless Power Transfer Platform for Smart Home Appliances. International Journal of Pharmacy & Technology. 8(3); 2016; .15669-15674.

  • Sajiv G. Unsupervised Clustering of Satellite Images in CIELab Color Space using Spatial Information Incorporated FCM Clustering Method. International Journal of Applied Engineering Research. 10(20); 2015.

  • Sathish BS, Ganesan P and Khamar Basha.Shaik. Color Image Segmentation based on Genetic Algorithm and Histogram Threshold. International Journal of Applied Engineering Research. 10(6); 2015; 123-127.

  • Thakur M, Raj I and Ganesan P. The cooperative approach of genetic algorithm and neural network for the identification of vehicle License Plate number. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). 2015; 1-6.

  • Ganesan P and B. S. Sathish. Automatic Detection of Optic Disc and Blood Vessel in Retinal Images using Morphological Operations and Ipachi Model. Research J. Pharm. and Tech. 10(8): August 2017; 2602-2607.

  • Wulandari, R. Sigit and M. M. Bachtiar, "Brain Tumor Segmentation to Calculate Percentage Tumor Using MRI," 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Bali, Indonesia, 2018, pp. 292-296.

  • Huang Meiyan, Wei Yang, Wu Yao, Jiang Jun, Chen Wufan, Qianjin Feng, "Brain Tumor Segmentation Based on Local Independent Projection-based Classification", IEEE Transactions on Biomedical Engineering, 2013.

  • Ganesan P, M.Ganesh , L.M.I.Leo Joseph and V.Kalist, “ Central Retinal Vein Occlusion: An Approach for the Detection and Extraction of Retinal Blood Vessels”, J. Pharm. Sci. & Res. Vol. 10(1), 2018, 192-195.

  • D. Bhattacharyya, T. H. Kim, "Brain tumor detection using MRI image analysis", Commun. Comput. Inform. Sci., vol. 151, pp. 307-314, 2011.

  • C. L. Biji, D. Selvathi, A. Panicker, "Tumor detection in brain magnetic resonance images using modified thresholding techniques", Commun. Comput. Inform. Sic., vol. 4, pp. 300-308, 2011.

  • Ganesan P,, “Detection and Segmentation of Retinal Blood Vessel in Digital RGB and CIELUV color space Fundus Images”, Research J. Pharm. and Tech. 11(6): 2018, 2326-2330.

  • T. M. Hsieh, Y. M. Liu, C. C. Liao, F. Xiao, I. J. Chiang, J. M. Wong, "Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing", BMC Med. Informat. Decision Making, vol. 11, pp. 54, 2011.

  • https://www.mathworks.com/help/matlab/


Abstract Views: 319

PDF Views: 0




  • A Simple Approach to Automated Brain Tumor Segmentation and Classification

Abstract Views: 319  |  PDF Views: 0

Authors

P. Ganesan
Department of Electronics and Communication Engineering, Vidya Jyothi Institute of Technology, Hyderabad, India
B. S. Sathish
Department of Electronics and Communication Engineering, Ramachandra College of Engineering, Eluru, India
R. Murugesan
Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, India

Abstract


Brain tumor is the abnormal growth of superfluous cells in central nervous system or brain. It is fact that brain tumor is second most common of cancer death among young people. There are two key categories of brain tumor as cancerous and non cancerous. The cancerous brain tumor is called as Malignant. It spreads very quickly and difficult to remove. The non-cancerous tumor, called Benign, growth rate is very slow as compared to malignant one and easy to remove. The work proposes a simple but more efficient method to detect and segment the brain tumor from the MRI image. The proposed work based on the threshold segmentation for the segmentation of the brain tumor. The MRI image of the brain is taken and processed in such a way so that the tumor is extracted from the given MRI image and displays the segmented part of the image which contains the tumor. The otsu global threshold performs tumor segmentation and image area opening applies to remove the small components form the tumor portion. The gray level co-occurrence matrix and other image quality measures computes (extracts) the features from the segmented image. Support vector machine classifier is finally classifies the tumor, either benign or malignant, based on the extracted features.

Keywords


Brain Tumor, Threshold, Principal Component Analysis, Discrete Wavelet Transform, Gray-Level Co-Occurrence Matrix, Support Vector Machine.

References