A geometric-statistical approach toward neuron matching

In the same vein as the genome project that mapped the genetic structure of complex organisms such as the mouse, those pursuing the neurome are seeking a map the neural anatomy. In this massive biological investigation, the tools of imaging and biological experimentation are outpacing the requisite tools in image analysis. In terms of comparing neurons, based on the geometrical structure and features within the structure, the accepted approaches are largely manual. In this paper, we propose a combined geometric-statistical approach toward automated neuron matching. We utilize the geometric information of a neuron and compute a pairwise distance histogram based on the geometric information, to find a similarity measure between neurons. The distribution function is so chosen such that it reflects the structural pattern of a set of neuronal points, and is rotationally invariant. Preliminary experiments on a set of three different classes of neurons, with six neurons in each class, provides evidence of efficacy, with the best two matches to a given query producing a retrieval error of 0% and the third best match producing an error of only 11.2%. In future work, the proposed method can be used as a component in the retrieval of similar neurons from neuronal database.