Abstract
Medical images that are acquired with reduced radiation exposure or following the administration of imaging agents with a low dose, are often known to experience problems by the noise stemming from acquisition hardware as well as psychological sources. This noise can adversely affect the quality of diagnosis, but also prevent practitioners from computing quantitative functional information. With a view to overcoming these challenges, the current paper puts forward optimization of multi-objective for denoising medical images within the wavelet domain. This proposed technique entails the use of genetic algorithm (GA) to get the threshold optimized within the denoising framework of wavelets. Two purposes are associated with this technique: First, its ability to adapt with different noise types of noise in the image without requiring prior information about the imaging process per se. In addition, it balances relevant diagnostic details' preservation against the reduction of noise by considering the optimization of the error factor of Liu and SNR as the foundation of objective function. According to the implementation of this method on magnetic resonance (MR) and ultrasound (US) images of the brain, a better performance has been observed as compared to the existing wavelet-based denoising methods with regard to quantitative and qualitative metrics.