DocumentCode
3309982
Title
A neural network approach for estimating large K distribution parameters
Author
Smolíková, Renata ; Wachowiak, Mark P. ; Zurada, Jacek M. ; Elmaghraby, Adel S.
Author_Institution
Comput. Sci. & Eng. Program, Louisville Univ., KY, USA
Volume
3
fYear
2001
fDate
2001
Firstpage
2139
Abstract
The K distribution has been proposed in the literature as a general speckle model for ultrasonic backscatter. The shape parameter of this distribution can be used to provide clinically important information on tissue density and regularity. A neural approach for parameter estimation is proposed, specifically for large values of the shape parameter. Experimental results on simulated images show that this approach compares favorably with other methods. Thus, neural networks can be used in conjunction with other approaches to accurately model speckle, and thereby to classify tissue
Keywords
Gaussian noise; backscatter; biological tissues; biomedical ultrasonics; feedforward neural nets; medical image processing; parameter estimation; probability; speckle; general speckle model; large K distribution parameters; neural network approach; parameter estimation; tissue density; tissue regularity; ultrasonic backscatter; Biomedical imaging; Computer science; Distributed computing; Frequency; Neural networks; Parameter estimation; Rayleigh scattering; Shape; Speckle; Ultrasonic imaging;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.938497
Filename
938497
Link To Document