Abstract
Denoising can be used as a tool to enhance image quality and enforce low radiation doses in X-ray medical imaging. The
effectiveness of denoising techniques relies on the validity of the underlying noise model. In full-field digital
mammography (FFDM) and digital breast tomosynthesis (DBT), calibration steps like the detector offset and flat-fielding
can affect some assumptions made by most denoising techniques. Furthermore, quantum noise found in X-ray
images is signal-dependent and can only be treated by specific filters. In this work we propose a pipeline for FFDM and
DBT image denoising that considers the calibration steps and simplifies the modeling of the noise statistics through
variance-stabilizing transformations (VST). The performance of a state-of-the-art denoising method was tested with and
without the proposed pipeline. To evaluate the method, objective metrics such as the normalized root mean square error
(N-RMSE), noise power spectrum, modulation transfer function (MTF) and the frequency signal-to-noise ratio (SNR)
were analyzed. Preliminary tests show that the pipeline improves denoising. When the pipeline is not used, bright pixels
of the denoised image are under-filtered and dark pixels are over-smoothed due to the assumption of a signal-independent
Gaussian model. The pipeline improved denoising up to 20% in terms of spatial N-RMSE and up to 15% in
terms of frequency SNR. Besides improving the denoising, the pipeline does not increase signal smoothing significantly,
as shown by the MTF. Thus, the proposed pipeline can be used with state-of-the-art denoising techniques to improve the
quality of DBT and FFDM images.
