title: Image denoising methods for tumor discrimination in high-resolution computed tomography.
abstract: Pixel accuracy in images from high-resolution computed tomography (HRCT) is ultimately limited by reconstruction error and noise. While for visual analysis this may not be relevant, for computer-aided quantitative analysis in either densitometric, or shape studies aiming at accurate results, the impact of pixel uncertainty must be taken into consideration. In this work, we study several denoising methods: geometric mean filter, Wiener filtering, and wavelet denoising. The performance of each method was assessed through visual inspection, profile region intensity analysis, and global figures of merit, using images from brain and thoracic phantoms, as well as several real thoracic HRCT images.
authors.affiliation: Department of Physics, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal. jsilva@ci.uc.pt
authors.forename: José Silvestre
authors.initials: JS
authors.lastname: Silva
authors.forename: Augusto
authors.initials: A
authors.lastname: Silva
authors.forename: Beatriz Sousa
authors.initials: BS
authors.lastname: Santos
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*