An Efficient Denoising Technique for CT Images using Window- based Multi-Wavelet Transformation and Thresholding

Abstract

Image denoising is one of the most significant tasks in image processing, analysis and image processing applications. Medical Imaging is one among the emerging application areas where the image denoising plays a vital role. In medical imaging, the acquisition techniques and systems introduce noises and artifacts in the medical image that leads to poor quality image. In this occasion, image denoising is an essential pre-requisite, especially in Computed Tomography, which is an important and most common modality in medical imaging. The significance of the denoising is mainly due to that the effectiveness of clinical diagnosis using CT image depends upon the quality of the image. In this paper, we propose an efficient noise reduction technique for CT images using window-based Multi-wavelet transformation and thresholding. The technique removes Additive white Gaussian noise from the CT images as well as it enhances the quality of the images. The proposed technique consists of three different stages of processing, namely, window-based multi-wavelet transformation and thresholding, reconstruction and quality enhancement. In the first two processes, the AWGN is effectively removed from CT images and the images are reconstructed. In the third process, the quality of the images is enhanced by means of filtering techniques. Hence, denoised and quality enhanced CT images can be obtained using the proposed multi-wavelet based denoising technique.