Prediction of Brain Tumor Image Segmentation using MRG and GLCM Algorithms
A.Srinivasa Reddy1, P. Chenna Reddy2
1A.Srinivasa Reddy, Department of CSE, KKR & KSR Institute of Technology & Sciences, Guntur (A.P), India.
2Dr. P.Chenna Reddy, Department of CSE, JNTU Ananthapur (A.P), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1159-1165 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6564048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The challenging unusual jobs in medical image processing are Brain tumor removal and its examination. Moreover, segmentation plays vital function in the handling of medical images, where the brain tumor MRI images were gathered and the tumor part is segmented efficiently from the original brain MRI images. In this paper, using our new proposed algorithm i.e. Modified Region Growing (MRG), the performance is evaluated where the whole anticipated method is implemented in the platform of MATLAB and the result analysis is done. Our new algorithm is compared with the existing K-Means and FCM methods using different parameters like PSNR, MSE, and SSIM. The results are shown using the comparison between input images and output images. Next, we can use GLCM (Gray Level Co Occurrence Matrix) for assessing the accuracy of brain tumor images. In this paper, we analyze all these methods in brief and try to understand how these methods will help us to go for better segmentation.
Keywords: Brain Tumor, Segmentation, MRI, Region Growing, K-Mean, FCM, GLCM.
Scope of the Article: Bio-Science and Bio-Technology