Call for Paper - June 2018 Edition
IJCA solicits original research papers for the June 2018 Edition. Last date of manuscript submission is May 21, 2018. Read More

Brain Tumor Detection from Pre-Processed MR Images Using Segmentation Techniques

2nd National Conference on Computing, Communication and Sensor Network
© 2011 by IJCA Journal
Number 2 - Article 1
Year of Publication: 2011
Sarbani Datta
Dr. Monisha Chakraborty

Sarbani Datta and Dr. Monisha Chakraborty. Brain Tumor Detection from Pre-Processed MR Images using Segmentation Techniques. IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network (CCSN) (2):1-5, 2011. Full text available. BibTeX

	author = {Sarbani Datta and Dr. Monisha Chakraborty},
	title = {Brain Tumor Detection from Pre-Processed MR Images using Segmentation Techniques},
	journal = {IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network (CCSN)},
	year = {2011},
	number = {2},
	pages = {1-5},
	note = {Full text available}


Magnetic resonance imaging (MRI) has become a common way to study brain tumor. In this paper we pre-process the two-dimensional magnetic resonance images of brain and subsequently detect the tumor using edge detection technique and color based segmentation algorithm. Edge-based segmentation has been implemented using operators e.g. Sobel, Prewitt, Canny and Laplacian of Gaussian operators. The color-based segmentation method has been accomplished using K-means clustering algorithm. The color-based segmentation carefully selects the tumor from the pre-processed image as a clustering feature. The present work demonstrates that the method can successfully detect the brain tumor and thereby help the doctors for analyzing tumor size and region. The algorithms have been developed on MATLAB version 7.6.0 (R2008a) platform.


  • M.R.Kaus, S.K.Warfield, A.Nabavi, P.M.Black, F.A.Jolesz, and R.Kikinis, “Automated segmentation of MR images of brain tumors,” Radiology, vol. 218, pp. 586-591, 2001.
  • S.K.Bandyopadhyay and D.Saha, “Brain region extraction volume calculation,” UNIASCIT, vol. 1, no. 1, pp. 44-48, 2011.
  • T.Logeswari and M.Karnan, “An improved implementation of brain tumor detection using segmentation based on hierarchical self organizing map,” Internatinal Journal of Computer Theory and Engineering, vol. 2, no. 4, pp. 1793-8201, August 2010.
  • C.Sai, B.S.Manjunath, and R.Jagadeesan, “Automated segmentation of brain MR images,” Pergamon, Pattern Recognition, vol. 28, no. 12, March 1995.
  • C.S.Tsai and C.C.Chang, “An improvement to image segment based on human visual system for object-based coding”, Fundamenta Informaticae, vol. 58, pp. 167–178, 2004.
  • C.W.Chen, J.Luo, K.J.Parker, “Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications”, IEEE Trans. on Medical Imaging, vol. 7, pp. 1673–1683, 1998.
  • H.P.Ng, H.H.Ong, K.W.C.Foong, P.S.Goh and W.L.Nowinski, “Medical image segmentation using K-means clustering and improved watershed algorithm”, IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 61–65, 2006.
  • R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, pp. 711-791, (2001).
  • Y.H.Hu, ECE 533 Image Processing Lecture Notes: Image Enhancement by Modifying Gray Scale of Individual Pixels, 2002-2003.
  • M.Prince, T.M.Grist, and J.F.Debatin, 3D Contrast MR Angiography, Springer-Verlag, NY, 1999.
  • E.Nadernejad, S.Sharifzadeh,, and H. Hassanpour, “Edge detection techniques: evaluation and comparisons”, Applied Mathematical Sciences, vol.2, pp. 1507-1520, 2008.
  • A.A. Moustafa and Z.A. Alqadi, “A practical approach of selecting the edge detector parameters to achieve a good edge map of the gray image”, J. Comput. Sci., vol. 5, pp. 355-362, 2009.
  • J.Canny, “A computational approach to edge detection”, IEEE Trans. Pattern Analysis and Machine Intalligence, vol. 8, pp.679-714, November 1986.