A Comparison of Filtering Techniques for Image Quality Improvement in Computed Tomography

Abstract

Computed Tomography (CT) is an important and most common modality in medical imaging. In CT examinations there is trade off between radiation dose and image quality. If radiation dose is decreased, the noise will unavoidably increase degrading the diagnostic value of the CT image and ifthe radiation dose is increased, the associated risk of cancer also increases especially in paediatric applications. Image filtering techniques perform image pre-processing to improve the quality of images. These techniques serve two major purposes. One is to maintain low radiation dose and another is to make subsequent phases of image analysis like segmentation or recognition easier or more effective. This paper presents the effect of noise reduction filter on CT images particularly that of anisotropic diffusion filter and Gaussian filter in combination with Prewitt operator. Anisotropic diffusion is Selective and nonlinear filtering technique which filters an image within the object boundaries and not across the edge orientation. Simulation results have shown that the anisotropic diffusion filter can effectively smooth noisy background, yet well preserve edge and fine details in the restored image. Gaussian filter smoothens the image while Prewitt operator detects the edges, so the combination of Gaussian filters and Prewitt operator works like a nonlinear filter.Thus these two filtering techniques improve an image quality and allow use of low dose CT protocol