Noise Reduction in Medical Images - comparison of noise removal algorithms -

The Medical community uses several image acquisition techniques for diagnosing and suggesting the corresponding therapies. Therefore the obtained images from clinical examinations should be treated to assist doctors in results interpretation. In this paper, we focus on denoising task in order to determine the benefits and drawbacks for each algorithm. For this, we used as database, images acquired from the most common techniques namely Magnetic Resonance (MR), Computed Tomography (CT), Ultrasounds, Scintigraphy and X-Ray. The effectiveness of discussed algorithms is compared on the basis of: Signal to Noise Ratio (SNR), Peak to Signal noise (PSNR), Root Mean square Error (RMSE) and the Mean Structure Similarity Index (MSSIM). Experimental results demonstrate that the NL-Means algorithm clearly outperforms the others denoising approaches for all noises levels.