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Curve interpolation model for visualising disjointed neural elements.

Rahim MS, Razzali N, Sunar MS, Altameem A, Rehman A - Neural Regen Res (2012)

Bottom Line: 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.The result shows about 82% acceptance and satisfaction rate.

View Article: PubMed Central - PubMed

Affiliation: UTMViCubeLab, Department of Computer Graphics and Multimedia, FSKSM, University of Technology, Skudai 81310, Malaysia.

ABSTRACT
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.

No MeSH data available.


Comparing effects using (A) Flat shaded polygons and (B) gouraud shading[36].
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Figure 8: Comparing effects using (A) Flat shaded polygons and (B) gouraud shading[36].

Mentions: Gouraud shading (GS) technique is applied to the neuron surface visualization in order to achieve realism at the connective edges and bounding surface without undergoing the accompanying heavy computational requirement of calculating lighting for each pixel at vertices. This approach was applied because it was able to provide the objects (i.e. the series of cylindrical that acts as the neuron morphology) connection toward effective realism. Comparison between curved surface rendering using Flat Shade Polygons and the Gouraud shading is shown in Figure 8.


Curve interpolation model for visualising disjointed neural elements.

Rahim MS, Razzali N, Sunar MS, Altameem A, Rehman A - Neural Regen Res (2012)

Comparing effects using (A) Flat shaded polygons and (B) gouraud shading[36].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4308766&req=5

Figure 8: Comparing effects using (A) Flat shaded polygons and (B) gouraud shading[36].
Mentions: Gouraud shading (GS) technique is applied to the neuron surface visualization in order to achieve realism at the connective edges and bounding surface without undergoing the accompanying heavy computational requirement of calculating lighting for each pixel at vertices. This approach was applied because it was able to provide the objects (i.e. the series of cylindrical that acts as the neuron morphology) connection toward effective realism. Comparison between curved surface rendering using Flat Shade Polygons and the Gouraud shading is shown in Figure 8.

Bottom Line: 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.The result shows about 82% acceptance and satisfaction rate.

View Article: PubMed Central - PubMed

Affiliation: UTMViCubeLab, Department of Computer Graphics and Multimedia, FSKSM, University of Technology, Skudai 81310, Malaysia.

ABSTRACT
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.

No MeSH data available.