Image processing and stereological techniques were used to characterize the heterogeneity of composite propellant and inform a predictive burn rate model. Composite propellant samples made up of ammonium perchlorate (AP), hydroxyl-terminated polybutadiene (HTPB), and aluminum (Al) were faced with an ion mill and imaged with a scanning electron microscope (SEM) and x-ray tomography (micro-CT). Properties of both the bulk and individual components of the composite propellant were determined from a variety of image processing tools. An algebraic model, based on the improved Beckstead-Derr-Price model developed by Cohen and Strand, was used to predict the steady-state burning of the aluminized composite propellant. In the presented model the presence of aluminum particles within the propellant was introduced. The thermal effects of aluminum particles are accounted for at the solid-gas propellant surface interface and aluminum combustion is considered in the gas phase using a single global reaction. Properties derived from image processing were used directly as model inputs, leading to a sample-specific predictive combustion model.