Rahim Mohd Shafry Mohd, Razzali Norhasana, Sunar Mohd Shahrizal, Altameem Ayman, Rehman Amjad
UTMViCubeLab, Department of Computer Graphics and Multimedia, FSKSM, University of Technology, Skudai 81310, Malaysia.
College of Applied Studies and Community Service, King Saud University, Riyadh 11451, Saudi Arabia.
Neural Regen Res. 2012 Jul 25;7(21):1637-44. doi: 10.3969/j.issn.1673-5374.2012.21.006.
Neuron cell are built from a myriad of axon and dendrite structures. It transmits electrochemical signals between the brain and the nervous system. Three-dimensional visualization of neuron structure could help to facilitate deeper understanding of neuron and its models. An accurate neuron model could aid understanding of brain's functionalities, diagnosis and knowledge of entire nervous system. Existing neuron models have been found to be defective in the aspect of realism. Whereas in the actual biological neuron, there is continuous growth as the soma extending to the axon and the dendrite; but, the current neuron visualization models present it as disjointed segments that has greatly mediated effective realism. In this research, a new reconstruction model comprising of the Bounding Cylinder, Curve Interpolation and Gouraud Shading is proposed to visualize neuron model in order to improve realism. The reconstructed model is used to design algorithms for generating neuron branching from neuron SWC data. The Bounding Cylinder and Curve Interpolation methods are used to improve the connected segments of the neuron model using a series of cascaded cylinders along the neuron's connection path. Three control points are proposed between two adjacent neuron segments. Finally, the model is rendered with Gouraud Shading for smoothening of the model surface. This produce a near-perfection model of the natural neurons with attended realism. The model is validated by a group of bioinformatics analysts' responses to a predefined survey. The result shows about 82% acceptance and satisfaction rate.
神经元细胞由无数的轴突和树突结构构成。它在大脑和神经系统之间传递电化学信号。神经元结构的三维可视化有助于更深入地理解神经元及其模型。一个准确的神经元模型有助于理解大脑的功能、诊断以及整个神经系统的知识。现有的神经元模型在真实性方面存在缺陷。在实际的生物神经元中，随着胞体延伸到轴突和树突，会持续生长；但是，当前的神经元可视化模型将其呈现为不连续的片段，这大大降低了有效真实性。在本研究中，提出了一种由边界圆柱体、曲线插值和高洛德着色组成的新重建模型来可视化神经元模型，以提高真实性。重建后的模型用于设计从神经元SWC数据生成神经元分支的算法。边界圆柱体和曲线插值方法用于沿着神经元的连接路径使用一系列级联圆柱体来改善神经元模型的连接片段。在两个相邻的神经元片段之间提出了三个控制点。最后，使用高洛德着色对模型进行渲染，以使模型表面平滑。这产生了一个具有逼真效果的近乎完美的自然神经元模型。该模型通过一组生物信息学分析师对预定义调查的回应进行了验证。结果显示接受率和满意率约为82%。
