Filtering impulse noise in medical images using information sets

Abstract

Abstract An efficient filtering algorithm is required to remove noise and simultaneously protect fine details and important features in the medical images. In this paper, a noise adaptive information set based switching median (NAISM) filter is proposed for the removal of impulse noise. NAISM filter is inspired from fuzzy switching median filter and works on the concept of information sets. Information sets are derived from fuzzy sets to deal with the uncertainty. It works in two phases; first phase identifies noisy pixels and second applies filtering based on an adaptive switching criterion. It is by virtue of this switching criterion and the local effective information surrounding the noisy pixel, the best calculated value replaces the noisy pixel in the selected window. The proposed information set based filter is capable of removing both low and high noise densities and can preserve image details better than the fuzzy filter. The applicability of the proposed filter is demonstrated on different datasets including Berkeley Segmentation Dataset (BSD), medical and real images. The qualitative and quantitative results demonstrate the effectiveness of the proposed approach in suppressing noise over the existing approaches.