title: Multiple resolution Bayesian segmentation of ultrasound images.
abstract: We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation for a probabilistic segmentation algorithm. Results appear to provide improvements over previous techniques in terms of low-contrast detail and accuracy.
authors.affiliation: Department of Electrical Engineering, University of Rochester, NY 14627, USA.
authors.forename: E A
authors.initials: EA
authors.lastname: Ashton
authors.forename: K J
authors.initials: KJ
authors.lastname: Parker
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D017548:Echocardiography, Transesophageal / Q000379:methods; D005704:Gallbladder / Q000000981:diagnostic imaging; D006801:Humans; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D014463:Ultrasonography / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data