Brain Tumor Detection and Segmentation in MR images Using GLCM and AdaBoost Classifier

Authors

  • Athira V S  ECE Department, Narayanguru College of Engineering, Anna University, Manjalumoodu, Tamil Nadu, India
  • Anand J Dhas  ECE Department, Narayanguru College of Engineering, Anna University, Manjalumoodu, Tamil Nadu, India
  • Sreejamole S S  ECE Department, Narayanguru College of Engineering, Anna University, Manjalumoodu, Tamil Nadu, India

Keywords:

AdaBoost Classifier, Brain tumor, Feature extraction, GLCM, Segmentation

Abstract

Brain tumor segmentation is one of the crucial procedures in surgical and treatment planning. Brain tumor segmentation using MRI has been an intense research area. Brain tumors can have various sizes and shapes and may appear at different locations. Varying intensity of tumors in brain magnetic resonance images (MRI) makes the automatic segmentation of tumors extremely challenging. There are various intensity based techniques which have been proposed to segment tumors on magnetic resonance images. Texture is one of most popular feature for image classification and retrieval. The multifractal texture estimation methods are more time consuming. A texture based image segmentation using GLCM (Gray-Level Co-occurence Matrix) combined with AdaBoost classifier is proposed here. From the MRI images of brain, the optimal texture features of brain tumor are extracted by utilizing GLCM. Then using these features AdaBoost classifier algorithm classifies the tumor and non-tumor tissues and tumor is segmented. This method provides more efficient brain tumor segmentation compared to the segmentation technique based on mBm and will provide more accurate result.

References

  1. V J Nagalkar and S S Asole, "Brain tumor detection using digital image processing based on soft computing", Journal of Signal and Image Processing, Vol.3, No:3, 2012
  2. D. Bhattacharayya and T. H. Kim, "Brain tumor detection using MRI image analysis," Communications in Computer and Information Science, vol. 151, pp. 307-314, 2011.
  3. R. Ratan, S. Sharma, and S. K. Sharma, "Brain tumor detection based on multi-parameter MRI image analysis," International journal on Graphics, Vision and Image Processing, vol.9, no. 3, pp. 9-11,2009.
  4. B. N. Saha, N. Ray, R. Greiner, A. Murtha, and H. Zhang, " Quick detection kof brain tumors and edemas: A bounding box method using symmetry," Computerized Medical Imaging and Graphics, vol. 36, no. 2, pp. 95-107,2012.
  5. Z. Iscan, Z. Dokur, and T. Olmez, "Tumor detection by using Zernike moments on segmented magnetic resonance brain images," Expert Systems with Applications, vol. 37, no. 3, pp. 25440-2549,2010.
  6. H. Khotanlou, O. Colliot, J. Atif, and I. Bloch, "3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models," Fuzzry Sets and Systems, vol. 160, no. 10, pp. 1457-1473,2009.
  7. Z. J. Wang, Q. M. Hu, K. F. Loe, A. Aziz, and W. L. Nowinski, "Rapid and automatic detection of brain tumors in MR Images," in Proc. Of SPIE, 2004.
  8. M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, " A brain tumor segmentation framework based on outlier detection,"  Med Image Anal, vol. 8, no. 3, pp. 275-83,Sep 2004.
  9. T. Wang, I. Cheng and A. Basu, "Fluid vector flow and applications in brain tumor segmentation," IEEE Transactions on Bio-medical Engineering, vol. 56, no. 3, pp. 781-9, Mar 2009.
  10. Y. Wu, W. Yang, J. Jiang, S. Q. Li, Q. J. Feng, and W. F. Chen, "Semi-automatic segmentation of  brain tumors using population and individual information," Journal of Digital Imaging, vol. 26, no. 4, pp. 786-796,2013.
  11. A. Hamamci, N. Kucuk, K. Karman, K. Engin, and G. Unal,"Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications," IEEE Transactions on Medical Imaging, vol. 31, no. 3, pp. 790-804, Mar 2012.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2015-06-25

Issue

Volume 1, Issue 3, May-June-2015

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Athira V S, Anand J Dhas, Sreejamole S S, " Brain Tumor Detection and Segmentation in MR images Using GLCM and AdaBoost Classifier, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 3, pp.142-146, May-June-2015.