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Proceedings Paper

Hyperspectral feature selection and fusion for detection of chicken skin tumors
Author(s): Songyot Nakariyakul; David Casasent
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Paper Abstract

We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral reflectance data. This allows for faster data collection than does fluorescence data. A chicken skin tumor is an ulcerous lesion region surrounded by a region of thickened-skin. Detection of chicken tumors is a difficult detection problem because the tumors vary in size and shape; some tumors appear on the side of the chicken. In addition, different areas of normal chicken skin have a variety of hyperspectral response variations, some of which are very similar to the spectral responses of tumors. Similarly, different tumors and different parts of a tumor have different spectral responses. Thus, proper classifier training is needed and many false alarms are expected. Since the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different, we train our feature selection algorithm to detect lesion regions and to detect thickened-skin regions separately; we then process the resultant images and we fuse the two HS detection results to reduce false alarms. Our new forward selection and modified branch and bound algorithm is used to select a small number of λ spectral features that are useful for discrimination. Initial results show that our method offers promise for a good tumor detection rate and a low false alarm rate.

Paper Details

Date Published: 30 March 2004
PDF: 12 pages
Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, (30 March 2004); doi: 10.1117/12.517443
Show Author Affiliations
Songyot Nakariyakul, Carnegie Mellon Univ. (United States)
David Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 5271:
Monitoring Food Safety, Agriculture, and Plant Health
George E. Meyer; Yud-Ren Chen; Shu-I Tu; Bent S. Bennedsen; Andre G. Senecal, Editor(s)

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