Performance Analysis of Kalman Filter in Computed Tomography Thorax for Image Denoising

Abstract

: Medical image processing is a very important field of study due to its large number of applications in human life. For diagnosis of any disease, several methods of medical image acquisition are possible such as Ultrasound (US), Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Depending upon the type of image acquisition, different types of noise can occur. Background: The most common types of noises in medical images are Gaussian noise, Speckle noise, Poisson noise, Rician noise and Salt & Pepper noise. The related noise models and distributions are described in this paper. We compare several filtering methods for denoising the mentioned types of noise. Objective: The main purpose of this paper is to compare well-known filtering methods such as arithmetic mean, median and enhanced lee filter with only rarely used filtering methods like Kalman filter as well as with relative new methods like Non-Local Means (NLM) filter. Methods: To compare these different filtering methods, we use comparative parameters like Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity (MSSIM), Edge Preservation Index (EPI) and the Universal Image Quality Index (UIQI). Results: The processed images are shown for a specific noise density and noise variance. We show that the Kalman filter performs better than Mean, Median and Enhanced Lee filter for removing Gaussian, Speckle, Poisson and Rician noise. Conclusion: Experimental results show that the Kalman filter provides better results as compared to other methods. It could be also a good alternative to NLM filter due to almost equal results and lower computation time.