American Journal of Nano Research and Applications

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A New Approach to Image Segmentation Mammogram

Received: 19 June 2015    Accepted: 07 July 2015    Published: 17 July 2015
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Abstract

Breast cancer continues to be one of the main causes of death among women. Various studies have confirmed that the early detection of sub-clinical cancers may improve the prognosis. X-ray mammography in this case is the best diagnostic technique. It’s based on the interaction of a cone beam X-ray with the mole tissue. The projection image obtained can be analyzed qualitatively by the radiologists. But, an automatic treatment and quantitative analysis of this kind of images is suitable. For this reason several studies are conducted to develop tools to help with diagnosis of this disease (CAD: Computer-Assisted Diagnosis). We propose in this paper a new method to segment mammographic images based partly on a pyramidal architecture. The original image is fragmented (quadtree) initially to homogeneous regions. Each region is then associated with a peak of graph. It gathers data within homogeneous groups named regions classes’ c, then we use HCA (Hierarchical classification ascendant) and k-means to find the optimal partition for the largest possible value of c at the initial stage. This technique gives good results, and allows calculating morphological parameters of the breast cancer.

DOI 10.11648/j.nano.20150304.12
Published in American Journal of Nano Research and Applications (Volume 3, Issue 4, July 2015)
Page(s) 78-81
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), 2024. Published by Science Publishing Group

Keywords

Mamography, Image Segmentation, K-means, Irregular Pyramid, HCA

References
[1] Krishna R . “The Possibilistic C-Means Algorithm: Insights and Recommendations”. IEEE transactions on Fuzzy Systems 1996.
[2] Hechmi SHILI*, Lotfi BEN ROMDHANE** et Béchir AYEB*(Une Nouvelle Approche pour la Segmentation des Images Mammographiques. Mars 2012)
[3] Mizutani and Miyamoto, 2005 Mizutani, K., Miyamoto, S. “Possibilistic Approach to Kernel-Based Fuzzy C-Means Clustering with Entropy Regularization”, Lecture Notes on Artificial Intelligence, vol. 3558, pp. 144–155. 2005.
[4] J. B. MacQueen (1967): "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability", Berkeley, University of California Press, 1:281-297.
[5] S. C. Johnson (1967): "Hierarchical Clustering Schemes" Psychometrika, 2:241-254.
[6] Verma, B. “Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms”. In: Artificial Intelligence in Medicine, Vol.42, pp. 67--79 (2008).
[7] Mizutani and Miyamoto, 2005 Mizutani, K., Miyamoto, S. “Possibilistic Approach to Kernel-Based Fuzzy C-Means Clustering with Entropy Regularization”, Lecture Notes on Artificial Intelligence, vol. 3558, pp. 144–155. 2005.
[8] TouJ.T. et Gonzalez R. « Pattern Recognition Principle », Addison-Wesley, Reading, MA, 1972.
[9] A. Montanvert, P. Meer, and A. Rosenfeld, “Hierarchical image analysis using irregular tessellations,” IEEE Transactions on Pattern Analysis andMachine Intelligence, vol. 13, no. 4, pp. 307–316, April 1991.
[10] J.M. Jolion and A. Montanvert, “The adaptive pyramid a framework for 2d image analysis,” CVGIP Image Understanding, vol. 55, no. 3, pp. 339–348, 1992.
[11] S. Lefèvre. “A new approach for unsupervised classification in image segmentation”. Advances in Knowledge Discovery and Management, Studies in Computational Intelligence. Springer-Verlag, 2009.
Author Information
  • LAboratory of Research on Optimization of Emergent Systems, Network and Imaging of computer science Department of Choua?bDoukkali University, EL Jadida, Morocco; LAboratory of Mathematics Applied on Physics and Industry, Mathematics Department of Choua?bDoukkali University, EL Jadida, Morocco

  • Radiology Departement Faculty of Medicine and Pharmacy of Hassan II University and Central Service of Radiology Ibn Rochd University Hospital Casablanca, Morocco

  • LAboratory of Mathematics Applied on Physics and Industry, Mathematics Department of Choua?bDoukkali University, EL Jadida, Morocco

  • Radiology Departement Faculty of Medicine and Pharmacy of Hassan II University and Central Service of Radiology Ibn Rochd University Hospital Casablanca, Morocco

  • LAboratory of Research on Optimization of Emergent Systems, Network and Imaging of computer science Department of Choua?bDoukkali University, EL Jadida, Morocco

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    Mohammed Rmili, Abdellatif Siwane, Fatiha Adnani, Fatiha Essodegui, Abdelmajid El Moutaouakkil. (2015). A New Approach to Image Segmentation Mammogram. American Journal of Nano Research and Applications, 3(4), 78-81. https://doi.org/10.11648/j.nano.20150304.12

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    ACS Style

    Mohammed Rmili; Abdellatif Siwane; Fatiha Adnani; Fatiha Essodegui; Abdelmajid El Moutaouakkil. A New Approach to Image Segmentation Mammogram. Am. J. Nano Res. Appl. 2015, 3(4), 78-81. doi: 10.11648/j.nano.20150304.12

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    AMA Style

    Mohammed Rmili, Abdellatif Siwane, Fatiha Adnani, Fatiha Essodegui, Abdelmajid El Moutaouakkil. A New Approach to Image Segmentation Mammogram. Am J Nano Res Appl. 2015;3(4):78-81. doi: 10.11648/j.nano.20150304.12

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  • @article{10.11648/j.nano.20150304.12,
      author = {Mohammed Rmili and Abdellatif Siwane and Fatiha Adnani and Fatiha Essodegui and Abdelmajid El Moutaouakkil},
      title = {A New Approach to Image Segmentation Mammogram},
      journal = {American Journal of Nano Research and Applications},
      volume = {3},
      number = {4},
      pages = {78-81},
      doi = {10.11648/j.nano.20150304.12},
      url = {https://doi.org/10.11648/j.nano.20150304.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.nano.20150304.12},
      abstract = {Breast cancer continues to be one of the main causes of death among women. Various studies have confirmed that the early detection of sub-clinical cancers may improve the prognosis. X-ray mammography in this case is the best diagnostic technique. It’s based on the interaction of a cone beam X-ray with the mole tissue. The projection image obtained can be analyzed qualitatively by the radiologists. But, an automatic treatment and quantitative analysis of this kind of images is suitable. For this reason several studies are conducted to develop tools to help with diagnosis of this disease (CAD: Computer-Assisted Diagnosis). We propose in this paper a new method to segment mammographic images based partly on a pyramidal architecture. The original image is fragmented (quadtree) initially to homogeneous regions. Each region is then associated with a peak of graph. It gathers data within homogeneous groups named regions classes’ c, then we use HCA (Hierarchical classification ascendant) and k-means to find the optimal partition for the largest possible value of c at the initial stage. This technique gives good results, and allows calculating morphological parameters of the breast cancer.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - A New Approach to Image Segmentation Mammogram
    AU  - Mohammed Rmili
    AU  - Abdellatif Siwane
    AU  - Fatiha Adnani
    AU  - Fatiha Essodegui
    AU  - Abdelmajid El Moutaouakkil
    Y1  - 2015/07/17
    PY  - 2015
    N1  - https://doi.org/10.11648/j.nano.20150304.12
    DO  - 10.11648/j.nano.20150304.12
    T2  - American Journal of Nano Research and Applications
    JF  - American Journal of Nano Research and Applications
    JO  - American Journal of Nano Research and Applications
    SP  - 78
    EP  - 81
    PB  - Science Publishing Group
    SN  - 2575-3738
    UR  - https://doi.org/10.11648/j.nano.20150304.12
    AB  - Breast cancer continues to be one of the main causes of death among women. Various studies have confirmed that the early detection of sub-clinical cancers may improve the prognosis. X-ray mammography in this case is the best diagnostic technique. It’s based on the interaction of a cone beam X-ray with the mole tissue. The projection image obtained can be analyzed qualitatively by the radiologists. But, an automatic treatment and quantitative analysis of this kind of images is suitable. For this reason several studies are conducted to develop tools to help with diagnosis of this disease (CAD: Computer-Assisted Diagnosis). We propose in this paper a new method to segment mammographic images based partly on a pyramidal architecture. The original image is fragmented (quadtree) initially to homogeneous regions. Each region is then associated with a peak of graph. It gathers data within homogeneous groups named regions classes’ c, then we use HCA (Hierarchical classification ascendant) and k-means to find the optimal partition for the largest possible value of c at the initial stage. This technique gives good results, and allows calculating morphological parameters of the breast cancer.
    VL  - 3
    IS  - 4
    ER  - 

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