Low-Dose CT Image Denoising using Image Decomposition and Sparse Representation

Abstract

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. The risk of high X-ray radiation may induce genetic, cancerous and other diseases, demanding the development of new image processing methods that are able to enhance the quality of low-dose CT images. However, lowering the radiation dose increases the noise in acquired images and hence affects important diagnostic information. This paper contributes an efficient denoising method for low-dose CT images. A noisy image is decomposed into three component images of low, medium and high frequency bands; noise is mainly presented in the medium and high component images. Then, by exploiting the fact that a small image patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches generated from noise-free images taken at nearly the same position with the noisy image, noise in these medium and high component images are effectively eliminated.Specifically, we give new solutions for image decomposition to easily control the filter parameters, for dictionary construction to improve the effectiveness and reduce the running-time. Instead of using a large dataset of patches, only a structured small part of patches extracted from the raw data is used to form a dictionary, to be used in sparse coding. In addition, we illustrate the effectiveness of the proposed method in preserving image details which are subtle but clinically important. Experimental results conducted on both synthetic and real noise data demonstrate that the proposed method is competitive with the state-of-the-art methods.