Performance evaluation of de-noised medical images after removing speckled noise by wavelet transform

In the healthcare community, the quality of the medical image is of prime concern to make accurate observations for diagnosis. Different types of noise such as Gaussian noise, impulse noise and speckle noise, etc., have been observed as a main cause for the quality degradation of the medical image. This degradation may further lead to the inconsistent information for the diagnosis, which will directly affect the patient's life. The removal of the noise from the medical image to maintain its quality has become a very tough task for the researchers and practitioners in the field of medical image processing. This paper aims on the comparative performance evaluation of various orthogonal and biorthogonal wavelet filters that are commonly used for the de-noising purpose based on some statistical parameters such as mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM) and correlation coefficient. The result of the present study depicts that biorthogonal 3.9 wavelet filters provide more precise image after the removal of noise as compare to other wavelet filters used.