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

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

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

	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}


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.


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