Image Denoising Comparative Performance Using Independent Component Analysis for Medical Images

Image denoising is a process in digital image processing aiming at the removal of noise. In medical imaging especially in Magnetic Resonance Imaging (MRI) images are typically corrupted with noise, which hinder the medical diagnosis based on these images. There are various techniques for medical images like Median filtering, PCA, Wavelet Thresholding and Independent Component Analysis (ICA). ICA separates unknown signal sources into statistically independent components without any prior knowledge. In this paper, we used ICA algorithm as denoising technique and compare its results with existing Wavelet Denoising. Performance results are evaluated in terms of metrics called Peak Signal-to-Noise Ratio (PSNR). Since noise in MR images is nongaussian, results show that ICA is a very appropriate analysis technique for eliminating noise in Medical images specially MRI.