• artificial intelligence applications
• data harmonization
• databases
• machine learning applications
• mammography
• medical physics
• representation

In this master thesis I modelled the image formation in mammography to evaluate the thickness of a material of interest in a multi-material object.
I started by working on the characterization of the mammographic unit of both the x-ray tube and its detector.
The equalization process of the image has been approached with the characterization of the several parameters like the dark current, the pixel intensity response to exposure at different kVp. This process showed a significant improvement especially at higher level of exposures. The output of the tube has then been measured and used in order to calibrate the simulated spectra. This has made the characterization of the energy response of the system possible. The scattering component of the radiation has been empirically characterized with the η factor.
Finally, the computation of the EGT has then been approached with two different methods the first of which considered the pixel intensity while the second made energy considerations. Where early results showed a big discrepancy between the reconstructed and real thickness of the material the scattering correction was implemented.