We propose a novel method for axonal bouton modeling and automated detection in populations of labeled neurons, as well as bouton distribution analysis for the study of neural circuit organization and plasticity. Since axonal boutons are the presynaptic specializations of neural synapses, their locations can be used to determine the organization of neural circuitry, and in time-lapse studies, neural circuit dynamics. We propose simple geometric models for axonal boutons that account for variations in size, position, rotation and curvature of the axon in the vicinity of the bouton. We then use the normalized cross-correlation between the model and image data as a test statistic for bouton detection and position estimation. Thus, the problem is cast as a statistical detection problem where we can tune the algorithm parameters to maximize the probability of detection for a given probability of false alarm. For example, we can detect 81% of boutons with 9% false alarm from noisy, out of focus, images. We also present a novel method to characterize the orientation and elongation of a distribution of labeled boutons and we demonstrate its performance by applying it to a labeled data set.