Fusion algorithm for poultry skin tumor detection using hyperspectral data

We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral (HS) reflectance data. Detection of chicken tumors is difficult because the tumors vary in size and shape; some tumors are small, early-stage tumor spots. We make use of the fact that a chicken skin tumor consists of a lesion region surrounded by a region of thickened skin and that the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different and train our feature selection algorithm to separately detect lesion regions and thickened-skin regions; we then fuse the two HS detection results to reduce false alarms. To the best of our knowledge, these techniques are new. Our forward selection and modified branch and bound algorithm is used to select a small number of lambda 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.