Two-photon calcium imaging (TPCI) is a functional neuroimaging technique that simultaneously reveals the function of small populations of cells as well as the structure of surrounding brain tissue. These unique properties cause TPCI to be increasingly popular for experimental basic neuroscience. Unfortunately, methodological development for data processing has not kept pace with experimental needs. I address this lack by developing and testing new methodology for several key tasks. Specifically, I address two primary analysis steps which are nearly universally required in early data processing: region of interest segmentation and motion correction. For each task I organize the sparse existing literature, clearly define the requirements of the problem, propose a solution, and evaluate it on experimental data. I develop MaSCS, an automated adaptable multi-class segmentation system that improves with use. I carefully define and describe the impact of motion artifacts on imaging data, and quantify the effects of standard and innovative motion correction approaches. Finally, I apply my work on segmentation and motion correction to explore one scientific target, namely discovering correlation-based cell clustering. I show that estimating such correlation-based clustering remains an open question, as it is highly sensitive to motion artifacts, even after motion correction techniques are applied. The contributions of this work include the organization of existing resources, methodological advances in segmentation, motion correction and clustering, and the development of prototype analysis software.