International Journal of Medical Imaging

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Synthesis of Mammographic Images Based on the Fractional Brownian Motion

Received: 25 January 2018    Accepted: 05 February 2018    Published: 27 February 2018
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Abstract

This paper presents a new approach for synthesizing breast tissue images based on a random fractal process, the fractional Brownian motion (fBm). This work deals with modeling Regions of Interest (ROIs) of mammographic images. Diverse synthetic ROIs were generated: healthy ones and others with microcalcifications according to fatty and dense tissue. Microcalcifications were injected in several dispositions in order to model benign and malignant cases. The aim of this study resides in two points: (1) the generation of synthetic images of mammograms for researchers and radiologists in order to test their tools and orient the choice of their parameters to enhance the diagnostic accuracy; and (2) to compare two microcalcification segmentation approaches: ‘Sq-Sq’ approach based on multifractal analysis and the ‘MM’ approach based on Mathematical Morphology. In fact, the results proved that the ‘Sq-Sq’ method can detect microcalcifications with different arrangements for any type of tissue and were evaluated using a qualitative test by an expert and a quantitative one based on the Area Overlap Measure (AOM) and the Dice coefficient. The ‘Sq-Sq’ approach yield a mean of 0.8±0.06 for AOM and 0.8446 for Dice coefficient for all segmented images.

DOI 10.11648/j.ijmi.20180601.11
Published in International Journal of Medical Imaging (Volume 6, Issue 1, March 2018)
Page(s) 1-8
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

Synthetic Images, fBm, Mammography, Microcalcifications, Segmentation

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Author Information
  • Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia

  • Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia

  • Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia

  • Research Unit Wavelet and Multifractal, Faculty of Science of Monastir, University of Monastir, Monastir, Tunisia

  • Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia

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  • APA Style

    Ines Slim Sahli, Hanen Bettaieb, Asma Ben Abdallah, Imen Bhouri, Mohamed Hedi Bedoui. (2018). Synthesis of Mammographic Images Based on the Fractional Brownian Motion. International Journal of Medical Imaging, 6(1), 1-8. https://doi.org/10.11648/j.ijmi.20180601.11

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    ACS Style

    Ines Slim Sahli; Hanen Bettaieb; Asma Ben Abdallah; Imen Bhouri; Mohamed Hedi Bedoui. Synthesis of Mammographic Images Based on the Fractional Brownian Motion. Int. J. Med. Imaging 2018, 6(1), 1-8. doi: 10.11648/j.ijmi.20180601.11

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    AMA Style

    Ines Slim Sahli, Hanen Bettaieb, Asma Ben Abdallah, Imen Bhouri, Mohamed Hedi Bedoui. Synthesis of Mammographic Images Based on the Fractional Brownian Motion. Int J Med Imaging. 2018;6(1):1-8. doi: 10.11648/j.ijmi.20180601.11

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  • @article{10.11648/j.ijmi.20180601.11,
      author = {Ines Slim Sahli and Hanen Bettaieb and Asma Ben Abdallah and Imen Bhouri and Mohamed Hedi Bedoui},
      title = {Synthesis of Mammographic Images Based on the Fractional Brownian Motion},
      journal = {International Journal of Medical Imaging},
      volume = {6},
      number = {1},
      pages = {1-8},
      doi = {10.11648/j.ijmi.20180601.11},
      url = {https://doi.org/10.11648/j.ijmi.20180601.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijmi.20180601.11},
      abstract = {This paper presents a new approach for synthesizing breast tissue images based on a random fractal process, the fractional Brownian motion (fBm). This work deals with modeling Regions of Interest (ROIs) of mammographic images. Diverse synthetic ROIs were generated: healthy ones and others with microcalcifications according to fatty and dense tissue. Microcalcifications were injected in several dispositions in order to model benign and malignant cases. The aim of this study resides in two points: (1) the generation of synthetic images of mammograms for researchers and radiologists in order to test their tools and orient the choice of their parameters to enhance the diagnostic accuracy; and (2) to compare two microcalcification segmentation approaches: ‘Sq-Sq’ approach based on multifractal analysis and the ‘MM’ approach based on Mathematical Morphology. In fact, the results proved that the ‘Sq-Sq’ method can detect microcalcifications with different arrangements for any type of tissue and were evaluated using a qualitative test by an expert and a quantitative one based on the Area Overlap Measure (AOM) and the Dice coefficient. The ‘Sq-Sq’ approach yield a mean of 0.8±0.06 for AOM and 0.8446 for Dice coefficient for all segmented images.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Synthesis of Mammographic Images Based on the Fractional Brownian Motion
    AU  - Ines Slim Sahli
    AU  - Hanen Bettaieb
    AU  - Asma Ben Abdallah
    AU  - Imen Bhouri
    AU  - Mohamed Hedi Bedoui
    Y1  - 2018/02/27
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    DO  - 10.11648/j.ijmi.20180601.11
    T2  - International Journal of Medical Imaging
    JF  - International Journal of Medical Imaging
    JO  - International Journal of Medical Imaging
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    EP  - 8
    PB  - Science Publishing Group
    SN  - 2330-832X
    UR  - https://doi.org/10.11648/j.ijmi.20180601.11
    AB  - This paper presents a new approach for synthesizing breast tissue images based on a random fractal process, the fractional Brownian motion (fBm). This work deals with modeling Regions of Interest (ROIs) of mammographic images. Diverse synthetic ROIs were generated: healthy ones and others with microcalcifications according to fatty and dense tissue. Microcalcifications were injected in several dispositions in order to model benign and malignant cases. The aim of this study resides in two points: (1) the generation of synthetic images of mammograms for researchers and radiologists in order to test their tools and orient the choice of their parameters to enhance the diagnostic accuracy; and (2) to compare two microcalcification segmentation approaches: ‘Sq-Sq’ approach based on multifractal analysis and the ‘MM’ approach based on Mathematical Morphology. In fact, the results proved that the ‘Sq-Sq’ method can detect microcalcifications with different arrangements for any type of tissue and were evaluated using a qualitative test by an expert and a quantitative one based on the Area Overlap Measure (AOM) and the Dice coefficient. The ‘Sq-Sq’ approach yield a mean of 0.8±0.06 for AOM and 0.8446 for Dice coefficient for all segmented images.
    VL  - 6
    IS  - 1
    ER  - 

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