Evaluation of the fractal dimension as a pattern recognition feature using neural networks

John S. DaPonte; Jo Ann Parikh; James Decker; Joseph N. Vitale

doi:10.1117/12.152517

2 September 1993 Evaluation of the fractal dimension as a pattern recognition feature using neural networks

John S. DaPonte, Jo Ann Parikh, James Decker, Joseph N. Vitale

Author Affiliations +

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152517
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

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John S. DaPonte, Jo Ann Parikh, James Decker, and Joseph N. Vitale "Evaluation of the fractal dimension as a pattern recognition feature using neural networks", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152517; https://doi.org/10.1117/12.152517

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