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Proceedings Paper

Ultrasonic image texture classification using Markov random field models
Author(s): John S. DaPonte; Jo Ann Parikh; Joseph N. Vitale; James Decker
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Paper Abstract

Over the past several years we have been interested in the supervised classification of ultrasonic images of the liver based on quantitative texture features. Our most recent efforts are concerned with the inclusion of features computed from Markov random fields. After adding four such features to our existing model containing 17 features, we employed stepwise discriminant analysis to identify the features that could best discriminate among 184 previously classified normal and abnormal ultrasonic images. Three of the four features derived from Markov random field models were identified by stepwise discriminant analysis as being good discrimination along with 6 existing features. From these results we constructed a backpropagation neural network with an input layer consisting of 9 nodes. We found that this new model yielded slightly better results when compared to earlier models. Our most recent results yielded a sensitivity of 81%, a specificity of 77% and an overall accuracy of 79%.

Paper Details

Date Published: 2 March 1994
PDF: 6 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169973
Show Author Affiliations
John S. DaPonte, Southern Connecticut State Univ. (United States)
Jo Ann Parikh, Southern Connecticut State Univ. (United States)
Joseph N. Vitale, Southern Connecticut State Univ. (United States)
James Decker, Southern Connecticut State Univ. (United States)


Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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