Poultry skin tumor detection in hyperspectral images using radial basis probabilistic neural network

Authors:
Intaek Kim;Chengzhe Xu;Moon S. Kim
Affiliations:
Department of Communication Engineering, Myongji University, Kyonggido, South Korea;Department of Communication Engineering, Myongji University, Kyonggido, South Korea;USDA ARS, BA, ANRI, ISL, Beltsville, MD
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 1
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Abstract

This paper presents a method for detecting poultry skin tumors using hyperspectral fluorescence image. New feature space is generated by the ratio of intensities of two bands, the combination of images such that their intensity ratios yield the least false detection rate is selected by minimizing overlap area of normal and tumor’s PDFs. Four feature images are chosen and presented as an input to a classifier based on the radial basis probability neural network. The classifier categorizes the input with three classes, where one is for tumor and two for normal skin pixels. The classification result based on this method shows the improved performance in that the number of false classification is reduced.