Poisson noise removal from mammogram using poisson unbiased risk estimation technique

Abstract

We present an experimental work on the denoising of mammogram with Poisson noise. Reviewing the literature, it is found that the denoising performance of the multiresolution tools like wavelet, contourlet and curvelet implemented on mammogram with Poisson noise is unique. The first part of the investigation deals with the confirmation of this exceptional performance with our result. The later half implements the recently developed denoising approach called the Poisson Unbiased Risk Estimation-Linear Expansion of Thresholds (PURE-LET) to the Poisson noise corrupted mammogram with an objective to improve the peak signal to noise ratio (PSNR) further. The PURE-LET successfully removes Poisson noise better than the traditional mathematical transforms already mentioned. The computation time and PSNR are also evaluated in the perspective of the cycle spinning technique. This validates the applicability and efficiency of the novel denoising strategy in the field of digital mammography.