The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis.