Photoacoustic systems can produce high-resolution, high-contracts images of vascular structures. To reconstruct images at very high-resolution, signals must be collected from many transducer locations, which can be time consuming due to limitations in transducer array technology, In this thesis a method is presented to discriminate between normal and abnormal tissue based on the structural morphology of vasculature and permits data to be acquired quickly. To demonstrate that the approach may be useful for cancer detection, a special simulator that produces photoacoustic signal from 3D models of vascular tissue is developed. Validation of the simulator is performed against a derived exact equation for finite-length cylindrical photoacoustic sources and through FEM models. Results show that is possible to differentiate tissue classed even when it is not possible to resolve individual blood vessels. Performance of the algorithm remains strong as the number of transducer locations decreases and in the presence of noise.