Two-dimensional shape classification using generalized Fourier representation and neural networks

Artur Chodorowski; Tomas Gustavsson; Ulf Mattsson

doi:10.1117/12.382910

14 April 2000 Two-dimensional shape classification using generalized Fourier representation and neural networks

Artur Chodorowski, Tomas Gustavsson, Ulf Mattsson

Proceedings Volume 3962, Applications of Artificial Neural Networks in Image Processing V; (2000) https://doi.org/10.1117/12.382910
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

A shape-based classification method is developed based upon the Generalized Fourier Representation (GFR). GFR can be regarded as an extension of traditional polar Fourier descriptors, suitable for description of closed objects, both convex and concave, with or without holes. Explicit relations of GFR coefficients to regular moments, moment invariants and affine moment invariants are given in the paper. The dual linear relation between GFR coefficients and regular moments was used to compare shape features derive from GFR descriptors and Hu's moment invariants. the GFR was then applied to a clinical problem within oral medicine and used to represent the contours of the lesions in the oral cavity. The lesions studied were leukoplakia and different forms of lichenoid reactions. Shape features were extracted from GFR coefficients in order to classify potentially cancerous oral lesions. Alternative classifiers were investigated based on a multilayer perceptron with different architectures and extensions. The overall classification accuracy for recognition of potentially cancerous oral lesions when using neural network classifier was 85%, while the classification between leukoplakia and reticular lichenoid reactions gave 96% (5-fold cross-validated) recognition rate.

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Artur Chodorowski, Tomas Gustavsson, and Ulf Mattsson "Two-dimensional shape classification using generalized Fourier representation and neural networks", Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); https://doi.org/10.1117/12.382910

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KEYWORDS

Neural networks

Feature extraction

Error analysis

Cancer

Network architectures

Resolution enhancement technologies

Binary data

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