Automated annotation and identification tools for quantitative study of the brain are in demand. Robust and scalable models for classification of neuron morphologies are necessary for the automation. The goal of this thesis is to introduce a robust and fast methodology based on minute local level features of 3D projection neurons for classification of into subtypes. This study proposes a "Codebook" or "Periodic Table" of local features of volumetric neurons. The idea is that every neuron subtype will have a unique combination of features from the Codebook to create its morphological features. Existing image segmentation based labeling and probabilistic models to assign neuroanatomical labels do not take into account morphological features at a local level. Morphological features of a neuron at a local level like axon-soma linkages and dendrite-soma linkages are crucial in determining the neuron morphology and hence its label or location. This thesis also highlights the potential of semi-supervised segmentation of volumetric (3D) neurons for automatic region identification and labeling with minimal data annotation
