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State Of Art of Medical Image Segmentation Techniques

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IJCA Proceedings on Recent Trends in Future Prospective in Engineering and Management Technology
© 2016 by IJCA Journal
RTFEM 2016 - Number 2
Year of Publication: 2016
Authors:
Shreya Chauhan
Kanchan Yadav
Anukrati Mishra

Shreya Chauhan, Kanchan Yadav and Anukrati Mishra. Article: State Of Art of Medical Image Segmentation Techniques. IJCA Proceedings on Recent Trends in Future Prospective in Engineering and Management Technology RTFEM 2016(2):4-7, July 2016. Full text available. BibTeX

@article{key:article,
	author = {Shreya Chauhan and Kanchan Yadav and Anukrati Mishra},
	title = {Article: State Of Art of Medical Image Segmentation Techniques},
	journal = {IJCA Proceedings on Recent Trends in Future Prospective in Engineering and Management Technology},
	year = {2016},
	volume = {RTFEM 2016},
	number = {2},
	pages = {4-7},
	month = {July},
	note = {Full text available}
}

Abstract

Segmentation is used as the first step in treatment and recognizing a disease by distinguishing the tissue borders. Thus, it is important to correctly perform segmentation so that the illness can be cured successfully. It even works when the brightness of the image becomes too low. In this paper, we will discuss different techniques to perform image segmentation.

References

  • http:/www. google. com/Wikipedia
  • A. M. Khan, Ravi. S "Image Segmentation Methods: A Comparative Study", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-3, Issue-4, September 2013
  • Keri Woods, "Genetic Algorithms: Colour Image Segmentation Literature Review", July 24, 2007.
  • W. Skarbek and A. Koschan. Colour image segmentation - a survey, 1994.
  • Dr. (Mrs. ) G. Padmavathi, Dr. (Mrs. ) P. Subashini and Mrs. A. Sumi "Empirical Evaluation of Suitable Segmentation Algorithms for IR Images", IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 4, No 2, July 2010.
  • X. Munoz, J. Freixenet, X. Cuf_?, J. Mart, "Strategies for image segmentation combining region and boundary information", Pattern Recognition Letters 24, page no 375–392, 2003.
  • Tianzi Jiang, Faguo Yang, Yong fan, David J. Evans,"Brain Tumor Image Segmentation using Parallel Genetic Algorithm" International Journal of Science and Modern Engineering (IJISME),2010
  • Wahba Marian, "An Automated Modified Region Growing Technique for Prostate Segmentation in Trans-Rectal Ultrasound Images", Master's Thesis, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada, 2008.
  • H. P. Narkhede, "Review of Image Segmentation Techniques", International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, Volume-1, Issue-8, July 2013
  • Zhang, Y. J, "An Overview of Image and Video Segmentation in the last 40 years", Proceedings of the 6th International Symposium on Signal Processing and Its Applications, pp. 144-151, 2001
  • Divya Kaushik, Utkarsha Singh e. t. al, "Medical Image Segmentation using Genetic Algorithm", International Journal of Computer Applications (0975 – 8887) Volume 81 – No 18, November 2013.
  • Keri Woods, "Genetic Algorithms: Colour Image Segmentation Literature Review", July 24, 2007
  • Falkenauer, E. , "Genetic Algorithms and Grouping Problems", John Wiley & Sons, Boston, 1998.
  • W. Frei, C. Chen, "Fast Boundary Detection: A Generalization and New Algorithm," IEEE Trans. Computers, vol. C-26, no. 10, pp. 988-998, Oct. 1977.
  • R. C. Gonzalez and R. E. Woods, "Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall", 2001, pp. 572-585.
  • W. K. Pratt, "Digital Image Processing". New York, NY: Wiley-Interscience, 1991, pp. 491-556.
  • Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing", third edition, Pearson publication.
  • G. M. N. R. Gajanayake, R. D. Yapa and B. Hewawithana "Comparison of Standard Image Segmentation Methods for Segmentation of Brain Tumors from 2D MR Images".
  • M. P. Gupta and M. M. Shringirishi, "Implementation of Brain tumor Segmentation in Brain MRI using K-means Clustering and fuzzy c-means algorithm", International Journal of Computers & Technology, vol. 5, no. 1,pp. 54-59,2013.
  • N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao, and Y. Zhu,"Kernal Feature Selection to fuse multi-spectral MRI imags for brain tumor segmentation,Computer Vision and Image Understanding,vol. 115,no. 2,pp. 256-269,2011.
  • R. Meenakshi and P. Anandhakumar, "Brain Tumor Identification in MRI with BPN Classifier and Orthonormal Operators",European Journal of Scientific Research,September 2012.
  • G. -C. Lin, W. -J. Wang, C. -C. Kang, and C. -M. Wang," Multispectral mr images segmentation based on fuzzy knowledge and modified seeded region growing",Magnetic Resonance Imaging,vol. 30,no. 2,pp. 230-246,2012
  • M. Kumar and K. K. Mehta," Texture based Tumor detection and automatic Segmentation using Seeded Region Growing Method",International Journal of Computer Technology and Applications,August 2011.