title: Estimation of K distribution parameters using neural networks.
abstract: The K distribution is an accurate model for ultrasonic backscatter. A neural approach is developed to estimate K distribution parameters. Accuracy and consistency of the estimates from simulated K and envelope data compare favorably with other techniques. Neural networks can potentially be used as a complementary technique for tissue characterization.
authors.affiliation: Computer Science and Engineering Program, University of Louisville, KY 40292, USA. mpwach01@athena.louisville.edu
authors.forename: Mark P
authors.initials: MP
authors.lastname: Wachowiak
authors.forename: Renata
authors.initials: R
authors.lastname: Smolíková
authors.forename: Jacek M
authors.initials: JM
authors.lastname: Zurada
authors.forename: Adel S
authors.initials: AS
authors.lastname: Elmaghraby
mesh_terms: D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014463:Ultrasonography / Q000379:methods* / Q000706:statistics & numerical data