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Neuron morphology data, Visualization, Shading, Bioinformatics

This study focused on Gouraud Shading approach to improve appearance of neuron visualization. Neuron visualization is a computational tool that able to describe, generate, store and render large set of three-dimensional neuronal morphology in a format that is compact, quantitative, and readily accessible to the neuroscientists. This tool enlightens its ability as a powerful computational modeling of neuronal morphology to explore greater understanding in neuron developmental processes and structure-function relationships. The areas of comparative neuron analysis can be aided thru the presenting knowledge of realism virtual neuron connection structure. Gouraud Shading is a visualization shading technique to perform a smooth lighting on the polygon surface without heavy computational requirement of calculating lighting for each pixel but at vertices only.However, after a thorough investigation, one of several problems discovered in neuron structure prediction is related to misleading in generating digitalized neuron raw data toward realistic neuron network visualization. For that reason, many approaches have been proposed in previous study in order to perform neuron morphology visualization based on stochastic sampling data of morphological measures from digital reconstructions of real neuron cells. Therefore, comparison among these approaches has been conducted as an important task to recognize a suitable approach and it is a preliminary of this research development prior to proceed for the next stage. This comparison result revealed a constraint to reconstruct neuron model towards greater realism with efficient flexible is still remains as an essential challenge in biological computing and visualization in order to provide abroad appearance of neuron knowledge distribution. As a proposal, Gouraud shading approach is applied for this purpose. In addition, the conclusion was summarized based on the comparing evaluation exercise between our framework result with result that has been produced from others three existing neuron visualization application. The comparison analysis has been done in term of reliability and smoothness of surface neuron presentation. Roughly the proposed framework achieved the objective to solve the problem encountered in presenting virtual neuron data.