Fuzzy inference rule based image despeckling using adaptive maximum likelihood estimation

Abstract

Despite multitude of research ponders on despeckling ultrasound images, contriving an efficient despeckling method still exist as an open challenge. The presence of speckle noise in ultrasound images complicates the accuracy of disease diagnosis. The classical Rayleigh Maximum Likelihood Estimation (RMLE) based despeckling filter was exclusively proposed to despeckle ultrasound images. It provides proper tradeoff between speckle suppression and edge preservation by discriminating the image region as edge and background correspondingly. Many despeckling filters utilize various statistical measures which remains uncertain in order to distinguish between image edge region and background region. This proposed filter harness the Fuzzy inference rules based image connectivity measure to avoid ambiguity in image region discrimination and adapts an appropriate tuning parameter to modify classical RMLE despeckling. The proposed method involves three steps in which the first step uses Fuzzy inference rules to categorize the type of image area as edge and background. Second step involves recursive optimal selection of appropriate filter tuning parameter by using adaptive technique. Third step involves estimation of noise-free pixels by using RMLE formulation. Quantitative evaluation was made by considering various performance metrics to compare the proposed filter and existing filters. The obtained result demonstrates that the proposed filter predominantly preserves the edges structures and clinical features.