Impulse Noise Removal from Medical Images Using Fuzzy Genetic Algorithm

Abstract

Medical images are analyzed for diagnosis of various diseases. But, they are susceptible to impulse noise. Noise removal can be done much more efficiently by a combination of image filters or a composite filter, than by a single image filter. Determining the appropriate filter combination is a difficult task. In this paper, we propose a technique that uses Fuzzy Genetic Algorithm to find the optimal composite filters for removing all types of impulse noise from medical images. Here, a Fuzzy Rule Base is used to adaptively change the crossover probability of the Genetic Algorithm used to determine the optimal composite filters. The results of simulations performed on a set of standard test images for a wide range of noise corruption levels shows that the proposed method outperforms standard procedures for impulse noise removal both visually and in terms of performance measures such as PSNR, IQI and Tenengrad values.