ABSTRACT

Medical practitioners are increasingly using digital images during disease diagnosis. Several state-of-the-art medical equipments are producing images of different organs, which are used during various stages of analysis. Examples of such devices include MRI, CT, ultrasound and X-Ray. Out of these, X-Ray is one the oldest and frequently used devices, as they are non-intrusive, painless and economical. The X-Ray images are normally affected by Poisson noise. The noise in the image has two disadvantages, the first being the degradation of the image quality and the second, more important, obscures important information required for accurate diagnosis. The main aim of any denoising algorithm is to remove noise while preserving important diagnostic data. This study combines two works that uses wavelets and Independent Component Analysis (ICA) to form a hybrid model that uses ICA technique coupled with Multiple Wavelet Denoising (MWD) Structure to remove noise. All the three works aim to remove Poisson noise from X-Ray images. Several experiments were conducted. The performance of the proposed system is analyzed in terms of Peak Signal to Noise Ratio and speed of denoising and a comparison is presented with the existing system.