Fluorescence microscopic imaging is widely used for pathological diagnosis of diseases in tissues and biomedical research purposes. Hyperspectral imagers with increased spectral and spatial resolution have the capacity to provide greater structural and molecular information. Algorithm-based analysis platforms capable of analyzing large biomedical hyperspectral datasets are unmet needs and have the potential to extract useful spectral-spatial information from complex tissues. We present an open-source data analysis approach to exploit the potential of hyperspectral fluorescence imaging and to extract unbiased and useful spectral-spatial information from the eye. We demonstrate distinctly different autofluorescence spectra for individual eye tissue types. Furthermore, the systematic segmentation method is used to classify tissue types based on their divergent autofluorescence spectra.