Microprocessor-based tissue classification using artificial neural net classifier

Author

Botros, N. ; Tee, H.

Author_Institution

Dept. of Electr. Eng., Southern Illinois Univ., Carbondale, IL, USA

fYear

1991

fDate

14-17 May 1991

Firstpage

80

Abstract

Presents an algorithm and instrumentation for classifying liver tissue abnormalities. The instrumentation used is a 50-MHz microcomputer-based data acquisition and analysis system. The primary functions of the system are to digitize the backscattered ultrasound signal from a human liver tissue phantom, process these digitized data in the frequency domain, and apply pattern recognition algorithms to classify the abnormalities of simulated liver tissues. The pattern recognition algorithm is based on a three-layer back-propagation artificial neural network. The results show that the algorithm works satisfactorily for classifying simulated normal liver tissue and three types of simulated abnormalities

Keywords

backpropagation; biomedical ultrasonics; data acquisition; image recognition; liver; medical image processing; microcomputer applications; neural nets; 50 MHz; artificial neural net classifier; back-propagation artificial neural network; backscattered ultrasound signal; frequency domain; liver tissue abnormalities; microcomputer-based data acquisition; normal liver tissue; pattern recognition; simulated abnormalities; Data acquisition; Data analysis; Frequency domain analysis; Humans; Imaging phantoms; Instruments; Liver; Pattern recognition; Signal processing; Ultrasonic imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1991., Proceedings of the 34th Midwest Symposium on

Conference_Location

Monterey, CA

Print_ISBN

0-7803-0620-1

Type

conf

DOI

10.1109/MWSCAS.1991.252131

Filename

252131