Medical Image Denoising using Fast Discrete Curvelet Transform

Abstract

Now-a-days, medical field plays a very crucial role in our daily life, as a part of it MRI (Magnetic resonance imaging) scans, CT (computed tomography) images, Ultrasound images etc. of the victim which are one of the main things that are to be determined correctly based on which the patient’s condition is concluded and treated. The main problem here occurs is for the original image where the image gets noisy and the features of the original image are lost due to many factors. So, here in our paper, we instigate the method of image denoising technique which helps to eliminate the noisy observations and other disturbances and reconstructs the original image very accurately. The image denoising is one of the important preprocessing steps in medical field image processing analysis. For this denoising method, we are going to use the Fast Discrete Curvelet Transform which is a multi-scale geometric transform and is designed to signify the image or video sequences at different scales and angles. also the performances of it by using fast Fourier discrete curve-let transform which is based on ridge-let analysis theory for denoising procedures and makes recommendations with the help of adaptive threshold algorithm which is applied on the image and gets the original image with effectiveness also retrieves the important detail features in the image and also the quality of the image to be recovered by using the parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE).