Band selection of hyperspectral images for automatic detection of poultry skin tumors

This paper presents a spectral band selection method for feature dimensionality reduction in hyperspectral image analysis for detecting skin tumors on poultry carcasses. A hyperspectral image contains spatial information measured as a sequence of individual wavelength across broad spectral bands. Despite the useful information for skin tumor detection, real-time processing of hyperspectral images is often a challenging task due to the large amount of data. Band selection finds a subset of significant spectral bands in terms of information content for dimensionality reduction. This paper presents a band selection method of hyperspectral images based on the recursive divergence for the automatic detection of poultry carcasses. For this, we derive a set of recursive equations for the fast calculation of divergence with an additional band to overcome the computational restrictions in real-time processing. A support vector machine is used as a classifier for tumor detection. From our experiments, the proposed band selection method shows high detection accuracy with low false positive rates compared to the canonical analysis at a small number of spectral bands. Also, compared with the enumeration approach of 93.75 % detection rate, our proposed recursive divergence approach gives 90.6 % detection rate, which is within the industry-accepted accuracy of 90-95%, while achieving the computational saving for real-time processing. Note to Practitioners-Hyperspectral fluorescence imaging offers an instant, noninvasive inspection method for detecting biomedical abnormalities. However, the huge amount of hyperspectral image data often makes real-time computer processing a challenging task. This paper suggests a band selection method of hyperspectral images based on the recursive divergence for the automatic detection of poultry carcasses. This method avoids transforming the original hyperspectral images to the feature space. Instead, it maximizes the class separability by considering the correlation information of spectral bands. In this paper, we mathematically characterize the use of divergence for band selection. Also, a set of recursive equations for the calculation of divergence with an additional band is derived to overcome the computational restrictions in real-time processing. The method may be extended to detect other biomedical abnormalities as well. In future research, we will incorporate the spatial and spectral information of the data in the development of appropriate band selection techniques for the hyperspectral data processing.