Gouraud shading to improve appearance of neuronal morphology visualization

Abstract

This study is focused on Gouraud Shading’s approach to improve appearance of neuron visualization. Neuron visualization is a computational tool that is 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. However, after a thorough investigation, one of the problems discovered in neuron structure prediction is related to misleading in generating digitalized neuron raw data toward realistic neuron morphology visualization. For that reason, many approaches have been proposed in previous studies in order to perform such visualization based on stochastic sampling data of morphological measures from digital reconstructions of real neuron cells. Therefore, comparison among these approaches has been conducted to recognize a suitable approach. It is still at a preliminary stage in research development. This exercise reveals a constraint to reconstruct neuron model towards greater realism efficiently is still remains as an essential challenge in biological computing and visualization to provide a broad appearance of neuron knowledge distribution. As a result, the areas of comparative neuron analysis can be aided through presenting the knowledge of realism virtual neuron morphology. As a proposal, Gouraud Shading’s approach is applied for this purpose. Gouraud Shading is a visualization shading technique to perform a smooth lighting on the polygon surface without heavy computational requirement in calculating lighting for each pixel but at vertices only. The conclusion is summarized based on verification exercise between our framework’s results with existing results from three neuron visualization applications. The comparison analysis is 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.