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Medical Image Segmentation by Active Contour Improvement

Received: 13 March 2017     Accepted: 22 March 2017     Published: 3 April 2017
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Abstract

This paper introduces a new medical image segmentation approach based on active contour improvement. The boundaries in brain images are detected using an original technique of active contour improved by a Region of Interest (ROI) extraction. We compare the results of the proposed model to Chane-Vese active contour model and Caselles’s et al. Geodesic active contour model. Experimental results of brain boundary localization on a national center of Oncology’s database demonstrate the promising performance of this method.

Published in American Journal of Software Engineering and Applications (Volume 6, Issue 2)
DOI 10.11648/j.ajsea.20170602.11
Page(s) 13-17
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

Medical Image Segmentation, Active Contours, Energy Minimization, ROI, Level Sets

References
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[9] X. Bresson and T. Chan, “Non-local Unsupervised Variational Image Segmentation Model,” UCLA CAM Report, pp. 08-67, 2008.
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Cite This Article
  • APA Style

    Abdelaziz Essadike, Elhoussaine Ouabida, Abdenbi Bouzid. (2017). Medical Image Segmentation by Active Contour Improvement. American Journal of Software Engineering and Applications, 6(2), 13-17. https://doi.org/10.11648/j.ajsea.20170602.11

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    ACS Style

    Abdelaziz Essadike; Elhoussaine Ouabida; Abdenbi Bouzid. Medical Image Segmentation by Active Contour Improvement. Am. J. Softw. Eng. Appl. 2017, 6(2), 13-17. doi: 10.11648/j.ajsea.20170602.11

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    AMA Style

    Abdelaziz Essadike, Elhoussaine Ouabida, Abdenbi Bouzid. Medical Image Segmentation by Active Contour Improvement. Am J Softw Eng Appl. 2017;6(2):13-17. doi: 10.11648/j.ajsea.20170602.11

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  • @article{10.11648/j.ajsea.20170602.11,
      author = {Abdelaziz Essadike and Elhoussaine Ouabida and Abdenbi Bouzid},
      title = {Medical Image Segmentation by Active Contour Improvement},
      journal = {American Journal of Software Engineering and Applications},
      volume = {6},
      number = {2},
      pages = {13-17},
      doi = {10.11648/j.ajsea.20170602.11},
      url = {https://doi.org/10.11648/j.ajsea.20170602.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20170602.11},
      abstract = {This paper introduces a new medical image segmentation approach based on active contour improvement. The boundaries in brain images are detected using an original technique of active contour improved by a Region of Interest (ROI) extraction. We compare the results of the proposed model to Chane-Vese active contour model and Caselles’s et al. Geodesic active contour model. Experimental results of brain boundary localization on a national center of Oncology’s database demonstrate the promising performance of this method.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Medical Image Segmentation by Active Contour Improvement
    AU  - Abdelaziz Essadike
    AU  - Elhoussaine Ouabida
    AU  - Abdenbi Bouzid
    Y1  - 2017/04/03
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajsea.20170602.11
    DO  - 10.11648/j.ajsea.20170602.11
    T2  - American Journal of Software Engineering and Applications
    JF  - American Journal of Software Engineering and Applications
    JO  - American Journal of Software Engineering and Applications
    SP  - 13
    EP  - 17
    PB  - Science Publishing Group
    SN  - 2327-249X
    UR  - https://doi.org/10.11648/j.ajsea.20170602.11
    AB  - This paper introduces a new medical image segmentation approach based on active contour improvement. The boundaries in brain images are detected using an original technique of active contour improved by a Region of Interest (ROI) extraction. We compare the results of the proposed model to Chane-Vese active contour model and Caselles’s et al. Geodesic active contour model. Experimental results of brain boundary localization on a national center of Oncology’s database demonstrate the promising performance of this method.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Optronic and Information Treatment Team, Atomic, Mechanical, Photonic and Energy Laboratory, Faculty of Science, Moulay Ismail University, Zitoune, Meknès, Morocco

  • Optronic and Information Treatment Team, Atomic, Mechanical, Photonic and Energy Laboratory, Faculty of Science, Moulay Ismail University, Zitoune, Meknès, Morocco

  • Optronic and Information Treatment Team, Atomic, Mechanical, Photonic and Energy Laboratory, Faculty of Science, Moulay Ismail University, Zitoune, Meknès, Morocco

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