Brain Mri Image Segmentation using Improved Sobel Method
P. Nagabushanam1, S. Satheesh Kumar2, J. Sunitha Kumar3, S. Thomas George4
1P.Nagabushanam, Department of EEE, Karunya Institute of Technology and Sciences, CBE, Hyderabad (Telangana), India.
2S. Satheesh kumar, Department of EEE, Karunya Institute of Technology and Sciences, CBE, Hyderabad (Telangana), India.
3J.Sunitha Kumari, Department of ECE,TKR TKR College of Engineering & Technology, Hyderabad, (Telangana), India.
4S.Thomas George, Department of EEE, Karunya Institute of Technology and Sciences, CBE, Hyderabad (Telangana), India.
Manuscript received on 24 September 2018 | Revised Manuscript received on 30 September 2018 | Manuscript published on 30 November 2018 | PP: 94-99 | Volume-7 Issue-4, November 2018 | Retrieval Number: E1806097418©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: Health care applications need correct segmentation on medical images which helps for correct diagnosis. Good quality segmentation can be done only by an efficient method. In this paper we have studied and evaluated three different segmentation techniques. We have presented an improved edge segmentation method for brain MRI images. The improved sobel algorithm makes use of sobel method with closed contour algorithm which will combinely help in maintaining uniformity in the regions. Image dependent method of thresholding helps in closed contour to fix up clear boundaries of different regions in an image. The algorithm is implemented in Matlab and performance is measured subjectively as well as objectively. For comparative analysis, we have used entrophy, correlation and energy of the three segmentations which show improved sobel method is better compared to watershed segmentation and sobel segmentation.
Keywords: Sobel segmentation, Watershed Segmentation, Improved Sobel Segmentation.
Scope of the Article: Data Mining Methods, Techniques, and Tools