Medical Images Denoising using Filters and Neural Network: Comparison through Implementations

Abstract

Wide variety of medical images are degraded by different types of noise such as Impulse noise, Gaussian noise, Poisson or a mixed of all. These noise disruptions typically result from sensor malfunctions, transmission issues, or environmental factors [1]. They provide an essential basis for clinical diagnosis and treatment. Unlike natural photographs, medical images often contain significant signal-related noise during their creation, resulting in lower contrast and more visible noise [3].No single method or procedure is effective for noise removal hence care should be taken before cleaning noisy area of image. Factors such as type of image (CT scan, Ultrasound, MRI etc) and type of noise such as (Gaussian, the impulsive and speckle noise etc) should be identified and based on the type of input, an appropriate method should be selected. For accurate segmentation of medical images through the automatic means,it is necessary to remove the noise from the images. There are many methods such as learning based (traditional methods) and non-learning based methods(using various types neural networks).To measure the denoising accuracy, parameters like Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used. This paper discusses the results obtained through the various methods and their comparison in terms of PSNR,SSIM. Removing noise is crucial for accurate segmentation of medical images. These noise-less images are to be treated as pre-processed images for the segmentation task.