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Proceedings Paper

New adaptive branch and bound algorithm for hyperspectral waveband selection for chicken skin tumor detection
Author(s): Songyot Nakariyakul; David Casasent
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Paper Abstract

Detection of skin tumors on chicken carcasses is considered. A chicken skin tumor consists of an ulcerous lesion region surrounded by a region of thickened-skin. We use a new adaptive branch-and-bound (ABB) feature selection algorithm to choose only a few useful wavebands from hyperspectral data for use in a real-time multispectral camera. The ABB algorithm selects an optimal feature subset and is shown to be much faster than any other versions of the branch and bound algorithm. We found that the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different; thus we train our feature selection algorithm to separately detect the lesion regions and thickened-skin regions of tumors. We then fuse the two HS detection results of lesion and thickened-skin regions to reduce false alarms. Initial results on six hyperspectral cubes show that our method gives an excellent tumor detection rate and a low false alarm rate.

Paper Details

Date Published: 23 October 2006
PDF: 11 pages
Proc. SPIE 6381, Optics for Natural Resources, Agriculture, and Foods, 63810S (23 October 2006); doi: 10.1117/12.686170
Show Author Affiliations
Songyot Nakariyakul, Carnegie Mellon Univ. (United States)
David Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 6381:
Optics for Natural Resources, Agriculture, and Foods
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)

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