Medical Image Denoising Using Synergistic Fibroblast Optimization Based Weighted Median Filter

Abstract

Image analysis involves processing and extraction of knowledge from images which is significantly useful in the large scale of medical and engineering applications. Image denoising is primarily applied in image analysis for degradation of noise, and thus improves the visual quality of images for information retrieval process. In the recent scenarios, due to the increase of complexity and diversity of digital images, removal of noise present in complicated images using classical filters becomes a quite challenge. The objective of this paper is to investigate the performance efficiency of a newly developed Synergistic Fibroblast optimization based Weighted Median Filter (SFO-WMF) for medical image analysis. Experiments are carried out with benchmark images, real time Magnetic Resonance Imaging (MRI) images, ultrasound breast cancer images and compared with conventional filters, namely, mean filter, median filter, wiener filter, Gaussian filter and weighted median filter. The performances of filters are validated using standard performance metrics and computational results demonstrated that the novel filter produces promising results and it outperforms than conventional filters in both qualitative and quantitative perspectives.