Title :
Neural estimation of scatterer density in ultrasound
Author :
Smolíková, Renata ; Wachowiak, Mark P. ; Tourassi, Georgia D. ; Zurada, Jacek M.
Author_Institution :
Comput. Sci. & Eng. Program, Louisville Univ., KY, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
Ultrasonic backscatter can provide information on the density of scatterers within biological media, and is therefore an important tool in tissue characterization. In this paper, a novel neural network approach to estimate scatterer density from generalized entropy is proposed. Neural estimation compares favorably with nonlinear least-squares models
Keywords :
backscatter; biological tissues; biomedical ultrasonics; entropy; function approximation; medical computing; neural nets; ultrasonic imaging; Shannon entropy; biological media; biomedical ultrasound; function approximation; neural network; scatterer density estimation; tissue characterization; tissue imaging; ultrasonic backscatter; Acoustic scattering; Backscatter; Biological system modeling; Biomedical engineering; Biomedical imaging; Entropy; Rayleigh scattering; Signal to noise ratio; Ultrasonic imaging; Ultrasonic transducers;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007773
