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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Detection and area calculation of brain tumour from MRI images using MATLAB

Suman Das, Nashra Nazim Siddiqui, Nehal Kriti and Surya Prakash Tamang, ,
Sikkim Manipal Institute of Technology, Sikkim-737132, India

The main objective of our task is to recognize a tumour and its quantifications from a particular MRI scan of a brain image using digital image processing techniques. The motivation of our work is to provide an efficient algorithm for detecting the brain tumour and calculating its growth. This research describes the proposed strategy to detect & extraction of brain tumour from patient’s MRI scan images of the brain. This method incorporates with some noise removal functions, segmentation and morphological operations which are the basic concepts of image processing. Detection and extraction of tumour from MRI scan images of the brain is done by using MATLAB software.

Suman Das, Nashra Nazim Siddiqui, Nehal Kriti, Surya Prakash Tamang, “Detection and area calculation of brain tumour from MRI images using MATLAB”, International Journal Of Computer Engineering In Research Trends, 4(1):37-40, January-2017. [InnoSpace-2017:Special Edition]

Keywords : MRI, Brain Tumour, digital image processing, segmentation, morphology, MATLAB.

[1]	 Dou, W., Ruan, S., Chen, Y., Bloyet, D., and Constans, J. M. (2007), “A framework of fuzzy information fusion for segmentation of brain tumor tissues on MR images”, Image and Vision Computing, 25:164–171. 
[2]	 T.Logeswari and M.Karnan, “An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map”, International Journal of Computer Theory and Engineering, Vol. 2, No. 4, August, 2010,pp.1793-8201. 
[3]	 R. Rajeswari and P. Anandhakumar, “Segmentation and Identification of Brain Tumor MRI Image with Radix4 FFT Techniques”, European Journal of Scientific Research, Vol.52 No.1 (2011), pp.100-109.
[4]	 S.Murugavalli, V.Rajamani, “A high speed parallel fuzzy c-mean algorithm for brain tumor segmentation”, ”BIME Journal”, Vol no: 06, Issue(1), Dec.,2006.
[5]	 Oelze, M.L,Zachary, J.F. , O'Brien, W.D., Jr., Differentiation of tumor types in vivo by scatterer property estimates and parametric images using ultrasound backscatter , on page(s) :1014 - 1017 Vol.1, 5-8 Oct. 2003. 
[6]	 T. Logeswari and M. Karnan, An improved implementation of brain tumor detection using segmentation based on soft computing, Second International Conference on Communication Software and Networks, 2010. ICCSN‟10.Page(s): 147-151. 
[7]	Devos, A, Lukas, L.,Does the combination of magnetic resonance imaging and spectroscopic imaging improve the classification of brain tumours?? On Page(s): 407 – 410, Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE, 1-5 Sept. 2004.
[8]	Mohammad Shajib Khadem, “MRI Brain image segmentation using graph cuts”, Master of Science Thesis in Communication Engineering, Department of Signals and Systems, Chalmers University Of Technology, Goteborg, Sweden, 2010. 

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