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# «Volume 3, Issue 9, September 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research ...»

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VII. METHODOLOGY In our research, we will also focus on similar line of implementation by finding and segregating the area of images which have tumor in it. First of all, we will fetch database of color images which have brain tumor in it. These images are process for noise removal for more accuracy by median filters. Further these images will be process with binary tree based quantization clustering process. This process will provide us the clusters to work on. Normally it distributes three clusters into shape. These clusters are process with ROI (reason of interest) technique to find the exact area to highlight and in our research case, it is tumor in brain. Finally segmentation process will be used to make it distinguish from other parts of the brain. The process by which the clusters would be formed can be explained by C ((x, - 4,)' el2 = e'R,,e Since R is symmetric, the solution is the eigenvector e, corresponding to the largest or principal eigenvector X, of R.

The total squared variation in the direction e, is therefore Once the principal eigenvector has been determined, points in C,

can be sorted into two sets C2, and C2, + I in the following way:

C2, = {s E C,: eix, I eiq,} C2n+l = {s E C,: etx, eiq,}.

Finally, the new statistics for each node may be calculated by first calculating R2,, m2,, and N2, for node 2n, and then applying the relations.

For calculation of various parameters like PSNR value and MSE value the following equations would be used:

–  –  –

## VIII. CONCLUSION

This Research is our continuous study and we will find the better results in highlighting the tumor in the segmented portion. In this paper we have studied various techniques for merging and segmentation process in the image processing for tumor detection. This Research is still in process and experimentation in running phase to test the proposed work.

REFERENCES M. Shasidhar, V.Sudheer Raja, B. Vijay Kumar,” MRI Brain Image Segmentation Using Modified Fuzzy CMeans Clustering Algorithm”, IEEE International Conference on Communication Systems and Network Technologies. pp.234-239, Vol.32, Issue.6, June 2011.

Erlend Hodneland,”Segmentation of Digital Images”, Dissertation report, University of Bergen, 22nd July 2003.

[2] Norbert Rőhrl, Jose Iglesias-Rozas and Galia Weidl, "Computer Assisted Classification of Brain Tumors", [3] Springer Berlin Heidelberg, pp. 55-60, 2008.

Sarbani Datta, Dr. Monisha Chakraborty,” Brain Tumor Detection from Pre-Processed MR Images using [4] Segmentation Techniques”, IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network, CCSN, 2011 Amir H. Foruzana, Reza A. Zoroofia, Masatoshi Horib, Yoshinobu Sato,”A knowledge-based technique for liver [5] segmentation in CT data”, Computerized Medical Imaging and Graphics, Vol.33, pp.567–587, 2009.

[6] Marius George Linguraru, William J. Richbourg, Jianfei Liu, Jeremy M. Watt, Vivek Pamulapati, Shijun Wang, and Ronald M. Summer,” Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor Segmentation”, IEEE Transactions On Medical Imaging, Vol. 31, No. 10, October 2012.

Page | 814 © 2013, IJARCSSE All Rights Reserved Malhotra et al., International Journal of Advanced Research in Computer Science and Software Engineering 3(9), September - 2013, pp. 811-815 Marcel Prastawa, Elizabeth Bullitt, Sean Ho, Guido Gerig,” A brain tumor segmentation framework based on [7] outlier detection”, Medical Image Analysis, Vol.4, pp.67-98, 2004.

Sang Chul Ahn,” Digital Image Processing”, Open access research, asc@imrc.kist.re.kr, UST Fall, 2007.

[8] [9] Ela Ostrovsky, Udi Zelig, Irina Gusakova, Samuel Ariad, Shaul Mordechai, Ilana Nisky and Joseph Kapilushnik, “Detection of Cancer Using Advanced Computerized Analysis of Infrared Spectra of Peripheral Blood”, IEEE Transactions on Biomedical Engineering, Vol. 60, pp. 343-353, February 2013.

[10] Marius George Linguraru, William J. Richbourg, Jianfei Liu, Jeremy M. Watt, Vivek Pamulapati, Shijun Wang, and Ronald M. Summers, “Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor Segmentation”, IEEE Transactions on Medical Imaging, Vol. 31, pp. 1965-1976, October 2012.

[11] Yifan Chen and Panagiotis Kosmas, “Detection and Localization of Tissue Malignancy Using Contrast-Enhanced Microwave Imaging”, IEEE Transactions on Biomedical Engineering, Vol. 59, pp. 766-776, March 2012.

[12] Karthikeyan Ganesan, U. Rajendra Acharya, Chua Kuang Chua, Lim Choo Min, K. Thomas Abraham, and KwanHoong Ng, “ Computer-Aided Breast Cancer Detection Using Mammograms”, IEEE Reviews in Biomedical Engineering, Vol. 6, pp. 77-98, 2013.

[13] O. Lezoray and C. Charrier, “ Color Image Segmentation Using Morphological Clustering and Fusion With Automatic Scale Selection”, Pattern Recognition Letters, Elsevier, Vol. 30, pp. 397-406, March 2009.

[14] Yufie Chen, Zhicheng Wang, Jinyong Hu, Weidong Zhab and Qidi Wu, “The Domain Knowledge Based GraphCut Model for Liver CT Segmentation”, Biomedical Signal Processing and Control, Elsevier, Vol. 7, pp. 591-598, November 2012.

[15] Li-Hong Juang and Ming-Ni Wu, “MRI Brain Lesion Image Detection based on Color-Converted K-means Clustering Segmentation”, Measurement, Elsevier, Vol. 43, pp. 941–949, April 2010.

[16] Marisol Martinez-Escobar, Jung Leng Foo and Eliot Winer, “ Colorization of CT images to Improve Tissue Contrast for Tumor Segmentation”, Computers in Biology and Medicine, Elsevier, Vol. 42, pp. 1170–1178, September 2012.

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