title: Bayesian denoising in digital radiography: a comparison in the dental field.
abstract: We compared two Bayesian denoising algorithms for digital radiographs, based on Total Variation regularization and wavelet decomposition. The comparison was performed on simulated radiographs with different photon counts and frequency content and on real dental radiographs. Four different quality indices were considered to quantify the quality of the filtered radiographs. The experimental results suggested that Total Variation is more suited to preserve fine anatomical details, whereas wavelets produce images of higher quality at global scale; they also highlighted the need for more reliable image quality indices.
authors.affiliation: Dip. Informatica, Università degli Studi di Milano, 20135 Milano, Italy. frosio@di.unimi.it
authors.forename: I
authors.initials: I
authors.lastname: Frosio
authors.forename: C
authors.initials: C
authors.lastname: Olivieri
authors.forename: M
authors.initials: M
authors.lastname: Lucchese
authors.forename: N A
authors.initials: NA
authors.lastname: Borghese
authors.forename: P
authors.initials: P
authors.lastname: Boccacci
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D017785:Photons; D016012:Poisson Distribution; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D019252:Radiography, Dental, Digital; D011862:Radiography, Panoramic