Call for Paper - September 2022 Edition
IJCA solicits original research papers for the September 2022 Edition. Last date of manuscript submission is August 22, 2022. Read More

Brain Segmentation using Support Vector Machine: Diagnostic Intelligence Approach

Print
PDF
IJCA Proceedings on International Conference on Benchmarks in Engineering Science and Technology 2012
© 2012 by IJCA Journal
ICBEST - Number 1
Year of Publication: 2012
Authors:
Manojkumar. S. Kathane
Vilas Thakare

Manojkumar.s.kathane and Vilas Thakare and. Article: Brain Segmentation using Support Vector Machine: Diagnostic Intelligence Approach. IJCA Proceedings on International Conference on Benchmarks in Engineering Science and Technology 2012 ICBEST(1):12-14, October 2012. Full text available. BibTeX

@article{key:article,
	author = {Manojkumar.s.kathane and Vilas Thakare and},
	title = {Article: Brain Segmentation using Support Vector Machine: Diagnostic Intelligence Approach},
	journal = {IJCA Proceedings on International Conference on Benchmarks in Engineering Science and Technology 2012},
	year = {2012},
	volume = {ICBEST},
	number = {1},
	pages = {12-14},
	month = {October},
	note = {Full text available}
}

Abstract

In the quantitative analysis of brain tissues, in magnetic resonance (MR) brain images, segmentation is the preliminary step. In this paper first we analyzed and compared various techniques used for Brain Image segmentation. Further it introduces an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images by using support vector machine (SVM) based classifier. A new and powerful kind of supervised machine learning with high generalization characteristics, is employed SVM. An iterative process is used for brain segmentation, so that the probabilistic maps of brain tissues will be updated at any iteration.

References

  • Kung-hao Liang and Tardi Tjahjadi, "Adaptive Scale Fixing for Multi-scale Texture Segmentation", IEEE Transactions on Image processing, Vol. 15, No. 1, January, pp. 249-256, 2006.
  • Mathews Jacob and Michael Unser, et al, "Design of Steerable Filters for Feature Detection Using Canny-Like Criteria", IEEE Transactions on Pattern Analysis and Machine n-Intelligence, Vol. 26, NO. 8, August, pp. 1007-1019, 2004.
  • Wiley Wang, et al. , "Hierarchical Stochastic Image Grammars for Classification and Segmentation", IEEE Transactions on Image processing, Vol. 15, No. 7, July, pp. 3033-3052, 2006.
  • D. Pham, C. Xu, J. Prince. Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337, 2000.
  • ZHOU Zhenyu, RUAN Zongcai, "Brain Magnetic Resonance Images Segmentation Based on Wavelet Method", Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
  • Pal NR, Pal SK, "A review of image segmentation techniques", Journal, Pattern Recognition, 1993, 26(9), pp. 1277-1294.
  • Yingli Zhang, Shengdong Nie*, Zhaoxue Chen, Wen Li "A Novel Segmentation Method of MR Brain Images Based on Genetic Algorithm", 1-4244-1120-3/07/$25. 00 ©2007 IEEE
  • A Huang, R. Abugharbieh, R. Tam "Automatic MRI Brain Tissue Segmentation using a Hybrid Statistical and Geometric Model " in Proc. 3rd International Symposium on Biomediacl Imaging, pp. 394-397, April2006
  • W. Bondareff, J Ravel, B. Woo, D L. Hausar, "Magnetic resonance imaging and the severity of dementia in older adults" Arch. Gen. Psychiatry, Vol47,pp47-51, Jan1990
  • Yingli Zhang, Shengdong Nie*, Zhaoxue Chen, Wen Li , "A Novel Segmentation Method of MR Brain Images Based on Genetic Algorithm", 1-4244-1120-3/07/$25. 00 ©2007 IEEE
  • Myung-Eun Lee1, Soo-Hyung Kim1, Wan-Hyun Cho2, Soon-Young Park3, and Jun-Sik Lim1, Segmentation of Brain MR Images using an Ant Colony Optimization Algorithm", 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering, 978-0-7695-3656-9/09 $25. 00 © 2009 IEEE
  • Arunava De, Rajib Lochan Das, Anup Kumar Bhattacharjee "Masking based Segmentation of Diseased MRI", 978-1-4244-5943-8/10/$26. 00 ©2010 IEEE
  • Keyvan Kasiri1, Kamran Kazemi1, 2 , Mohammad Javad Dehghani1, Mohammad Sadegh Helfroush1 "Atlas-based Segmentation of Brain MR Images Using Least Square Support Vector Machines", Image Processing Theory, Tools and Applications, 978-1-4244-7249-9/10/$26. 00 ©2010 IEEE
  • N. Cristianini, J. Shawe-Taylor, "An Introduction to support vector machines", Cambridge, UK: Cambridge Univ. Press, 2000.