Use of Wavelet Multiresolution Analysis to Reduce Radiation Dose in Digital Mammography

This paper investigates the use of a wavelet multiresolution analysis to reduce noise in mammographic images acquired with low levels of radiation dose. We studied the use of a wavelet denoising technique to filter the quantum noise that is incorporated in mammographic images when the radiation dose is reduced. Results were obtained by denoising a set of mammographic images acquired with different levels of radiation exposure, using an anthropomorphic breast phantom. Parameters of the algorithm were adjusted to provide more efficient reduction of noise without blurring or insertion of artifacts. We used the Anscombe transformation before denoising to convert the Poisson signal-correlated noise into an approximately additive white Gaussian noise. Evaluation of denoising performance were conducted by comparing image quality indexes between mammograms acquired with normal radiation dose and those acquired at lower doses levels, after denoising by the proposed technique.