An adaptive weighted denoising filter framework for impulse and shot noise: a mammogram image applications

The breast screening contraption has not yet became an effective diagnosis method for the detection of abnormalities. This is mainly due to the physical interference from the screening system; these tribulations usually develop a noise due to electric charge and also occur due to photon counting in optical screening machine. This work focuses on denoising the mammogram breast image to a greater extends with two-step process. In the first phase, mammogram image dataset is subjected to neural network to detect the corrupted image from non-noisy images by using different feature set. Then, average weighted of four different filters such as geometric-mean (GM) filter, decision-based median (DBM) filter, directional weighted median (DWM) filter and frost filter is applied to impulse and shot noise pixels, to preserve its corrupted edges and to avoid smoothing out of details. Additionally, this combined filter is subjected to exponential transformation to yield the enhanced output. The proposed filter is applied to these two noises and compared with existing filters with respective quality factors such as peak to signal noise ratio (PSNR) and mean absolute error (MAE). The outcome result shows that the proposed methods yield promising than the existing filters for mammogram images.