| Peer-Reviewed

Automatic Brain Tumor Detection in MRI Using Image Processing Techniques

Received: 6 January 2017     Accepted: 3 February 2017     Published: 1 March 2017
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Abstract

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.

Published in Biomedical Statistics and Informatics (Volume 2, Issue 2)
DOI 10.11648/j.bsi.20170202.16
Page(s) 73-76
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

MR Images, Support Vector Machine (SVM), Anisotropic Diffusion Filter, Brain Tumor Detection

References
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Cite This Article
  • APA Style

    Mariam Saii, Zaid Kraitem. (2017). Automatic Brain Tumor Detection in MRI Using Image Processing Techniques. Biomedical Statistics and Informatics, 2(2), 73-76. https://doi.org/10.11648/j.bsi.20170202.16

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    ACS Style

    Mariam Saii; Zaid Kraitem. Automatic Brain Tumor Detection in MRI Using Image Processing Techniques. Biomed. Stat. Inform. 2017, 2(2), 73-76. doi: 10.11648/j.bsi.20170202.16

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    AMA Style

    Mariam Saii, Zaid Kraitem. Automatic Brain Tumor Detection in MRI Using Image Processing Techniques. Biomed Stat Inform. 2017;2(2):73-76. doi: 10.11648/j.bsi.20170202.16

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  • @article{10.11648/j.bsi.20170202.16,
      author = {Mariam Saii and Zaid Kraitem},
      title = {Automatic Brain Tumor Detection in MRI Using Image Processing Techniques},
      journal = {Biomedical Statistics and Informatics},
      volume = {2},
      number = {2},
      pages = {73-76},
      doi = {10.11648/j.bsi.20170202.16},
      url = {https://doi.org/10.11648/j.bsi.20170202.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170202.16},
      abstract = {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.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Automatic Brain Tumor Detection in MRI Using Image Processing Techniques
    AU  - Mariam Saii
    AU  - Zaid Kraitem
    Y1  - 2017/03/01
    PY  - 2017
    N1  - https://doi.org/10.11648/j.bsi.20170202.16
    DO  - 10.11648/j.bsi.20170202.16
    T2  - Biomedical Statistics and Informatics
    JF  - Biomedical Statistics and Informatics
    JO  - Biomedical Statistics and Informatics
    SP  - 73
    EP  - 76
    PB  - Science Publishing Group
    SN  - 2578-8728
    UR  - https://doi.org/10.11648/j.bsi.20170202.16
    AB  - 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.
    VL  - 2
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer Engineering, Faculty of Electronic and Electrical Engineering, Tishreen University, Latakia, Syria

  • Department of Computer Engineering, Faculty of Electronic and Electrical Engineering, Tishreen University, Latakia, Syria

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