Hyperspectral imaging is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from hyperspectral imaging often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450 to 900 nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during the animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have the potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.