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IJSTR >> Volume 8 - Issue 7, July 2019 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

A Review Of Fully Automatic MRI Based Brain Tumor Segmentation Approaches

[Full Text]



A.R.Deepa, W.R. Sam Emmanuel



Medical Resonance Imaging, Brain tumor, classification, Feature Selection, Tumor Detection.



Brain tumor is an abnormal disease and its early detection is very important to save life. In MRI, the tumor region can be detected by segmentation. Manually, the segmentation or extraction of tumor from MRI is possible to diagnosis. MRI scans provide very detailed images of most of the important organs and tissues in our body. Many types of automated segmentation algorithms have been presented. For conveying information the medium of images are considered to be more important. The algorithms can predict better classification technique to extract tumor parts



[1] Iscan, Z, Dokur, Z and Ölmez, T. “Tumor detection by using Zernike moments on segmented magnetic resonance brain images”. Expert Systems with Applications, vol.37, no.3, pp. 2540-2549, 2010.
[2] Pereira, S, Pinto, A, Alves, V and Silva, C. A. “Brain tumor segmentation using convolutional neural networks in MRI images”. IEEE transactions on medical imaging, vol.35, no.5, pp. 1240-1251, 2016.
[3] Parisot, S, Duffau, H, Chemouny, S and Paragios, N. “Graph-based detection, segmentation & characterization of brain tumors”. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on IEEE, pp. 988-995, 2012.
[4] Logeswari T and Karnan M. “An improved implementation of brain tumor detection using soft computing”. In Communication Software and Networks, 2010. ICCSN'10. Second International Conference on IEEE. pp. 147-151, 2010.
[5] Işın, A, Direkoğlu, C and Şah, M. “Review of MRI-based brain tumor image segmentation using deep learning methods”. Procedia Computer Science, vol.102, pp.317-324, 2016.
[6] Lahmiri, S and Boukadoum, M. “Brain MRI classification using an ensemble system and LH and HL wavelet sub-bands features”. In Computational Intelligence in Medical Imaging (CIMI), 2011, IEEE Third International Workshop on IEEE, pp. 1-7, 2011.
[7] Padma A and Sukanesh R. “Automatic classification and segmentation of brain tumor in CT images using optimal dominant gray level run length texture features”. International Journal of Advanced Computer Science and Applications. Vol.2, no.10, 2011.
[8] Salwe, S, Raut, R and Hajare, P. “Brain tumor pixels detection using adaptive wavelet based histogram thresholding and fine windowing”. In Information Technology (InCITe)-The Next Generation IT Summit on the Theme-Internet of Things: Connect your Worlds, International Conference on IEEE, pp. 256-260, 2016.
[9] Kong, Y, Deng, Y and Dai, Q. “Discriminative clustering and feature selection for brain MRI segmentation”. IEEE Signal Processing Letters, vol.22, no.5, pp.573-577, 2015.
[10] Chaddad, A, Zinn, P. O and Colen, R.R. “Brain tumor identification using Gaussian Mixture Model features and Decision Trees classifier”. In Information Sciences and Systems (CISS), 2014 48th Annual Conference on IEEE, pp. 1-4, 2014.
[11] Kavitha, A. R, Chellamuthu, C and Rupa, K. “An efficient approach for brain tumour detection based on modified region growing and neural network in MRI images”. In Computing, Electronics and Electrical Technologies (ICCEET), 2012 International Conference on IEEE, pp. 1087-1095, 2012.
[12] Ghanavati, S., Li, J, Liu, T., Babyn, P. S., Doda, W and Lampropoulos, G. 2012, “Automatic brain tumor detection in magnetic resonance images”. In Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium, pp. 574-577.
[13] Deepak, K. S, Gokul, K, Hinduja, R and Rajkumar, S. “An efficient approach to predict tumor in 2D brain image using classification techniques”. In Information Communication and Embedded Systems (ICICES), 2013 International Conference on IEEE. pp. 559-564, 2013.