A survey on medical image denoising using optimisation technique and classification

In the field of medical science and technology, Image is often subjected to various types of noise distortion during the process of collection, acquisition, and transmission. The images plays an vital role in examining the patients trouble. While examining, the image comprises of more noises. These noises are the major factor affecting the quality of the image which has greatly impeded people from extracting the useful information from the image. In order to overcome this kind of obstacle we apply image denoising. The main intension of image denoising is to restore the original image without noise from the noising image and also the same time to maintain the detailed information of the image as much as possible. In this paper, we provide the combination of cuckoo search algorithm and artificial neural network where the noise in the image can be filtered and removed effectively using adaptive non-linear Zernike filter. The simulation result shows that the algorithm proposed in this paper can maintain the edges of the images and other important features while removing the noise, so as to obtain better denoising affect. The quality of the resultant image is being measured by using Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE).