Volume 6, Issue 1, March 2018, Page: 1-8
Synthesis of Mammographic Images Based on the Fractional Brownian Motion
Ines Slim Sahli, Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia
Hanen Bettaieb, Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia
Asma Ben Abdallah, Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia
Imen Bhouri, Research Unit Wavelet and Multifractal, Faculty of Science of Monastir, University of Monastir, Monastir, Tunisia
Mohamed Hedi Bedoui, Faculty of Medecine of Monastir, University of Monastir, Monastir, Tunisia
Received: Jan. 25, 2018;       Accepted: Feb. 5, 2018;       Published: Feb. 27, 2018
DOI: 10.11648/j.ijmi.20180601.11      View  1313      Downloads  64
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.
Synthetic Images, fBm, Mammography, Microcalcifications, Segmentation
To cite this article
Ines Slim Sahli, Hanen Bettaieb, Asma Ben Abdallah, Imen Bhouri, Mohamed Hedi Bedoui, Synthesis of Mammographic Images Based on the Fractional Brownian Motion, International Journal of Medical Imaging. Vol. 6, No. 1, 2018, pp. 1-8. doi: 10.11648/j.ijmi.20180601.11
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