When light is incident upon tissue, imaging contrast can be obtained from a range of interactions including absorption, scattering and fluorescence. Clinical optical imaging systems are typically optimized to report on a single contrast source, for example, using standard RGB cameras to produce white light reflectance images or filter-based approaches to extract fluorescence emissions. Hyperspectral imaging has the potential to over-come the need for specialized instrumentation, by sampling spatial and spectral information simultaneously. In particular, spectrally resolved detector arrays (SRDAs) now monolithically integrate spectral filters with CMOS image sensors to provide a robust, compact and low cost solution to video rate hyperspectral imaging. However, SRDAs suffer from a significant limitation, which is the inherent tradeoff between spatial and spectral resolution. Therefore, the properties of the SRDA including the number of filters, their wavelength and bandwidth, needs be optimized for tissue imaging. To achieve this, we have developed a software framework to optimize spectral band selection, simulating the hyperspectral sample illumination, data acquisition and spectral unmixing processes. Our approach shows early promise for selecting appropriate spectral filters, which allows us to maintain high spatial resolution for imaging.