Volume 2, Issue 2, June 2017, Page: 73-76
Automatic Brain Tumor Detection in MRI Using Image Processing Techniques
Mariam Saii, Department of Computer Engineering, Faculty of Electronic and Electrical Engineering, Tishreen University, Latakia, Syria
Zaid Kraitem, Department of Computer Engineering, Faculty of Electronic and Electrical Engineering, Tishreen University, Latakia, Syria
Received: Jan. 6, 2017;       Accepted: Feb. 3, 2017;       Published: Mar. 1, 2017
DOI: 10.11648/j.bsi.20170202.16      View  1726      Downloads  195
The research offers a fully automatic method for tumor segmentation on Magnetic Resonance Images MRI. In this method, at first in the preprocessing level, anisotropic diffusion filter is applied to the image by 8-connected neighborhood for removing noise from it. In the second step, using Support Vector Machine SVM Classifier for tumor detection accurately. After creating the appropriate mask image, based on the symmetry property in axial and coronary magnetic resonance images. The tumor detected and segmented (Dice coefficient > 0.90) in a few seconds. The method applied on several MR images with different types regardless of the degree of complexity in those images.
MR Images, Support Vector Machine (SVM), Anisotropic Diffusion Filter, Brain Tumor Detection
To cite this article
Mariam Saii, Zaid Kraitem, Automatic Brain Tumor Detection in MRI Using Image Processing Techniques, Biomedical Statistics and Informatics. Vol. 2, No. 2, 2017, pp. 73-76. doi: 10.11648/j.bsi.20170202.16
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This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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