title: Bayesian 2-D deconvolution: a model for diffuse ultrasound scattering.
abstract: Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle and significantly reduces the diagnostic value of the images. To remove both blur and speckle, we have developed a new statistical model for diffuse scattering in 2-D ultrasound radio frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio frequency ultrasound images are presented and compared with results produced by Wiener filtering.
authors.affiliation: Department of Mathematical Sciences, NTNU, N-7491 Trondheim, Norway. Oddvar.Husby@math.ntnu.no
authors.forename: O
authors.initials: O
authors.lastname: Husby
authors.forename: T
authors.initials: T
authors.lastname: Lie
authors.forename: T
authors.initials: T
authors.lastname: Langø
authors.forename: J
authors.initials: J
authors.lastname: Hokland
authors.forename: H
authors.initials: H
authors.lastname: Rue
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012542:Scattering, Radiation; D014463:Ultrasonography / Q000379:methods*