Hyperspectral feature selection and fusion for detection of chicken skin tumors

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.

Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system

“…It was successfully implemented for predicting beef tenderness (Cluff et al, 2008;Peng and Wu, 2008), classification and prediction of marbling, colour, texture and exudation, drip loss, pH in pork (Qiao et al, 2007a,b,c), contaminants and tumor detection in chicken (Lawrence et al, 2004;Nakariyakul and Casasent, 2004;Park et al, 2006) and assessment of water and fat contents in fish fillets (ElMasry and Wold, 2008).…”

Recent Advances in Data Mining Techniques and Their Applications in Hyperspectral Image Processing for the Food Industry

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“…The CV alone has been used in previous studies for feature selection [20] and feature extraction [21,22].…”