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Classification of ultrasonic image texture by statistical discriminant analysis and neural networks

✍ Scribed by John S. DaPonte; Porter Sherman

Publisher: Elsevier Science
Year: 1991
Tongue: English
Weight: 727 KB
Volume: 15
Category: Article
ISSN: 0895-6111
DOI: 10.1016/0895-6111(91)90100-a

No coin nor oath required. For personal study only.

✦ Synopsis

In this paper the ability of two common statistical discriminant analysis procedures are compared with two commercial neural network software packages. The major objective of this study was to determine which of the procedures could best discriminate between normal and abnormal ultrasonic liver textures. The same set of features were input into both statistical discriminant analysis procedures and both neural network models. Preliminary results have found the restricted Coulomb Energy (RCE) neural network model to have a testing accuracy of 90.6% which is approximately 10% better than any of the other techniques investigated.

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