Author(s): G. Merlin Sheeba, M. S. Godwin Premi, G. Mary Valantina, Z. Mary Livinsa

Email(s): merlinsheebu@gmail.com

DOI: 10.5958/0974-360X.2020.00330.3   

Address: G. Merlin Sheeba*, M. S. Godwin Premi, G. Mary Valantina, Z. Mary Livinsa
School of Electrical and Electronics, Sathyabama Institute of Science and Technology, Chennai.
*Corresponding Author

Published In:   Volume - 13,      Issue - 4,     Year - 2020


ABSTRACT:
Brain tumor segmentation in MRI images is one of the challenging tasks in medical field. In this work, an efficient brain tumor is detection methodology is proposed for the MRI images. Image preprocessing filters are used to remove noises from the images. Contextual clustering extracts the tumor regions from the images Object labeling is also performed following the contextual clustering. The simulation results show that the proposed technique has achieved 94% of efficiency.


Cite this article:
G. Merlin Sheeba, M. S. Godwin Premi, G. Mary Valantina, Z. Mary Livinsa. Brain Tumor Segmentation using Contextual Clustering and Object Labeling in MRI. Research J. Pharm. and Tech. 2020; 13(4):1833-1836. doi: 10.5958/0974-360X.2020.00330.3


REFERENCES:
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2.    Sucharita, V., Rao, P. V., Bhattacharyya, D., and Kim, T. H. (2016). Classification of Penaeid Prawn Species using Radial basis Probabilistic Neural Networks and Support Vector Machines. International Journal of Bio-Science and Bio-Technology, 8(1), 255-262.
3.    Padma, A., and Sukanesh, D. R. (2011). Automatic diagnosis of abnormal tumor region from brain computed tomography images using wavelet based statistical texture features. arXiv Preprint arXiv:1109.1067.
4.    De Boer, R., Van Der Lijn, F., Vrooman, H. A., Vernooij, M. W., Ikram, M. A., Breteler, M. M., and Niessen, W. J. (2007, April). Automatic Segmentation of Brain Tissue and White matter Lesions In MRI. In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 652-655). IEEE.
5.    Anbeek, P., Vincken, K. L., and Viergever, M. A. (2008). Automated MS-lesion segmentation by k-nearest neighbor classification. MIDAS Journal.
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