NeuroSoL: Automated classification of neurons using the sorted Laplacian of a graph
One of the most exciting open problems in biological image analysis is the categorization of neurons based on observed morphology. Complex arborization patterns, varied orientations, different sizes, nonidentical alignments of local branches act as barriers to a systematic and quantitative analysis of neurons. This open problem demands a refined approach to capture salient and robust, global and local features to compare digitally-traced 3D neuron atlases. We propose NeuroSoL to consolidate global as well as local morphological and geometrical features by leveraging a specialized graph framework. Specifically, without imposing any restriction on the structure and connectivity, we use fully-sorted and mixed-sorted Laplacians of the neuron graph and the corresponding complementary graph. For meaningful clustering of neurons, we also develop a similarity metric after retrieving optimal local alignment between the feature descriptors of two neurons. Results on neuron datasets show the efficacy of our approach over state-of-the-art techniques.