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Quantitative Analysis of Metastasis Brain Tumor and its Area Estimation in MR Images

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
K. Vidyasagar, A. Bhujangarao, T. Madhu
10.5120/ijca2016905950

K Vidyasagar, A Bhujangarao and T Madhu. Article: Quantitative Analysis of Metastasis Brain Tumor and its Area Estimation in MR Images. International Journal of Computer Applications 139(14):40-46, April 2016. Full text available. BibTeX

@article{key:article,
	author = {K. Vidyasagar and A. Bhujangarao and T. Madhu},
	title = {Article: Quantitative Analysis of Metastasis Brain Tumor and its Area Estimation in MR Images},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {14},
	pages = {40-46},
	month = {April},
	note = {Full text available}
}

Abstract

Metastasis brain tumor lops multiple tumors at asymmetrical location of the human brain. MRI Imaging is one of the prudent mechanisms to extract the tumor regions and to map the brain for diagnosing. For the better diagnosis, one must detect the tumor accurately and need to calculate the area and volume of the tumor exactly. Here in this letter, we proposed a novel resolution enhancement technique to improve the quality of MR brain image and optimized hybrid clustering with region split and merge algorithm to detect the tumor cells from the original MR images and to estimate the tumors from different locations. Simulation results show that the proposed algorithm has performed superior to conventional clustering algorithms such as Fuzzy C-means (FCM), K- Means and even optimized pillar algorithm.

References

  1. Yu-Hsiang Wang,” tutorial on Image segmentation”, National Taiwan University.
  2. B.naresh Kumar “ M.Sailaja “ An Automated 3D Segmented and DWT Enhanced Model for Brain MRI”, International Journal of scientific & Engineering Research , Vol.3, 2012.
  3. Samir kumar Bandopadyaya “ Image Enhancement Technique applied to low-Field MR Brain Images”, International Journal of Computer Applications, Vol. 15, feb 2011
  4. Sunaya U.Shirodkar,” Image Resolution Enhancement using various wavelet Transfers”, International journal of advances in science Engineering and Technology, Vol.1, 2014.
  5. V.C.MIAINDARGI, A.P.MANE “Decimated and Un-Decimated Wavelet Transforms based Image Enhancement”, International journal of industrial Electrical, Electronics, Control and Robotics, Vol.03,Issue.05,2013
  6. B.Sivakumar, S.Nagaraj “Discrete and stationary wavelet decomposition for image Resolution Enhancement” International Journal of Engineering trends and Technology , Vol. 4 ,2013
  7. Mr.G.M.Khaire, R.P.Shelkikar,” Resolution Enhancement of images with interpolation and DWT –SWT Wavelet domain components”, International Journal of Application or Innovation in Engineering and Management, Vol.2, 2013.
  8. Manisha Bhagwatl, R.K.Krishna& V.E.Pise, "Image Segmentation by Improved Watershed Transformation in Programming Environment MATLAB" International Journal of Computer Science & Communication Vol. 1, No. 2, pp. 171-174, 2010. 
  9. M.H. Fazel Zarandia, M. Zarinbala, M. Izadi, "Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach," Applied soft computing, pp: 285-294, 2011
  10. S. Zulaikha BeeviM, Mohamed Sathik, "An Effective Approach for Segmentation of MRI Images: Combining Spatial Information with Fuzzy C-Means Clustering" European Journal of Scientific Research, Vol. 41, No.3, pp.437-451, 2010. 
  11. S. Mary Praveena, Dr.I1aVennila, "Optimization Fusion Approach for Image Segmentation Using K-Means Algorithm" International Journal of Computer Applications, Vol 2, No.7, June 2010. 
  12. M. Masroor Ahmed & Dzulkifli Bin Mohammad, "Segmentation of Brain MR Images for Tumor Extraction by Combining K-means Clustering and Perona-Malik Anisotropic Diffusion Model" International Journal of Image Processing, Vol. 2, No. 1, 2010 
  13. Tse-Wei Chen, Yi-Ling Chen, Shao-Yi Chien, "Fast Image Segmentation Based on K-Means Clustering with Histograms in HSV Color Space" Journal of Scientific Research, Vol. 44 No.2, pp.337-351, 2010. 
  14. Anil Z Chitade, " Colour based image segmentation using k-means clustering" International Journal of Engineering Science and Technology Vol. 2(10), 5319-5325, 2010. 
  15. Selvakumar, J., Lakshmi, A., Arivoli, T., “Brain Tumor segmentation and its area Calculation in Brain MR images using K-means Clustering and Fuzzy C-means algorithm”, International Conference on Advances in Engineering, Science and Management (ICAESM), pp: 186-190, 2012.
  16. Barakbah, A.R., Kiyoki. Y., “A Pillar algorithm for K-means Optimization by Distance Maximization for Initial Centroid Designation”, IEEE Symposium on Computational Intelligence and Data Mining, pp: 61-68, 2009.
  17. K. Vidyasagar, Dr. A. Bhujangarao, Dr. T. Madhu,” Brain tumor detection and its area estimation using Pillar K-Means Algorithm”, International journal of Engineering sciences research , Vol. 05,march 2014

Keywords

Metastasis brain tumor, DWT, SWT, Interpolation, Image Segmentation, FCM, K-means, Optimized pillar algorithm