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 :
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