Noise Reduction in Medical X-Ray Images Using Wavelet and Neural Networks

Abstract

Noise reduction in x-rays has attracted many researcher in recent years. This noise is produced duo to reduction of X-ray powers. Increasing the X-ray powers threatens the human life. Wavelet domain threshold level proposed by many researchers for this noise reduction. However, the many cases this approach is invalid because the noise distribution function is poison. In this research study we used the wavelet domain based on a genetic algorithm to tune the BayesShrink threshold. To increase the clarity an image, we used a multi-layer perceptron neural network. However, this neural network and other approaches are not able to eliminate large amount of noises. To overcome this kind of noise, we used a Directional adaptive median filter. Because the edges of images may be vanished by this method, we reconstructed the edge level at the end of process. The simulation results indicate that our approach provides more clear images than many of other methods in the literature such as PSNR, MSR and CNR.