Digital Imaging plays an essential role throughout the major regions of life such as clinical diagnosis. However, it faces the trouble of Gaussian noise. Noise corrupts both images and videos equally. The aim of Denoising algorithm must be to remove these kinds of noise. Image denoising should be used because a new noisy image is just not pleasant to watch. Inclusion of, some fine details in the image could possibly be confused with the noise or perhaps vice-versa. Image denoising is a critical task throughout Medical applications, where the complexity regarding noise is predominant, as well as, the contrast regarding medical images tend to be moreover low caused by various photograph acquisition. In the past two decades, denoising is completed by this multi-resolution change like Wavelet change. Denoising pictures using Curvelet change approach has been widely utilized in many fields with its ability to have highly excellent images. Curvelet change is better than wavelet in the expression regarding image advantage, because such geometry attribute of challenge, has obtained achievements now in photograph denoising in the medical field. Here, medical images are denoised with various noise estimators using Curvelet transform. These different noise estimators are compared and measured using image quality metrics such as Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The experimental result proves to be a significant one.