Edge-preserving denoising for segmentation in CT-images

Abstract

In the clinical environment the segmentation of organs is an increasingly important application and used, for example, to restrict the perfusion analysis to a certain organ. In order to automate the time-consuming segmentation process denoising techniques are required, which can simultaneously remove the locally varying and oriented noise in computed tomography (CT) images and preserve edges of relevant structures. We analyze the suitability of different edge-preserving noise reduction methods to be used as a pre-processing step for Geodesic Active Contours (GAC) segmentation. Two popular methods, bilateral filtering and anisotropic diffusion, are compared to a wavelet-based approach, which is adjusted to the CT-specific noise characteristics. We show that robust segmentation results for different organs at varying noise levels can only be achieved using the wavelet-based denoising. Furthermore, the optimal selection of parameters for the bilateral filter and the anisotropic diffusion is highly dependent on the dataset and the segmentation task.