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Proceedings Paper

Evaluation of the fractal dimension as a pattern recognition feature using neural networks
Author(s): John S. DaPonte; Jo Ann Parikh; James Decker; Joseph N. Vitale
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Paper Abstract

In the past fractal dimension has often been computed using a stochastic approach based on a random walk process, which has been found to be very time consuming. More recently, mathematical morphology has been used to compute the fractal dimension in a more timely fashion. This paper describes how the fractal dimension computed using mathematical morphology can be used in the texture analysis of ultrasonic imagery. The discriminatory ability of the fractal dimension as a pattern recognition feature is evaluated and compared to more traditional parameters. This analysis includes comparisons with statistical features in which each parameter is treated as an independent variable and in which interactions between those variables are evaluated. Pattern recognition techniques include Stepwise Discriminant Analysis, Linear Discriminant Analysis, and Nearest Neighbor Analysis in addition to Backpropagation Neural Network Classifiers. Our results identify the fractal dimension as one of the most important parameters for distinguishing between normal and abnormal livers. In this study, consisting of 186 images, a significant statistical difference was found for both the mean and standard deviation of the fractal dimension between the normal and abnormal groups using parametric and nonparametric statistical techniques.

Paper Details

Date Published: 2 September 1993
PDF: 11 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152517
Show Author Affiliations
John S. DaPonte, Southern Connecticut State Univ. (United States)
Jo Ann Parikh, Southern Connecticut State Univ. (United States)
James Decker, Southern Connecticut State Univ. (United States)
Joseph N. Vitale, Southern Connecticut State Univ. (United States)


Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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