Curve interpolation model for visualising disjointed neural elements

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.

Keywords: Gouraud shading; bounding cylinder; curve interpolation; neural regeneration; reconstruction model.

Figure 1

Sample cell of neuronal morphology used for this study[17].

Figure 2

Digital representation of neuron morphology (right panel) from “SWC” format data (left panel)[13].

Figure 3

Proposed Integration approach of bounding cylinder and curve interpolation for extending the series of inter-disconnected cylinders.

Figure 4

Illustration of locator's bounding height, the length of neuron segment (or known as locator) from start coordinate to end coordinate

Figure 5

Illustration of locator's bounding radius (RL), the radius of cylinder bounding the neuron segment (also known as locator).

Figure 6

Illustration of Locator's curve interpolation bounding the neuron segment (also known as locator).

Figure 7

Proposed curve interpolation model.

Figure 8

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

Figure 9

System interface for Action-User key-in neuron file morphologies from format SWC.

Figure 10

Result of neuron presentation and its manipulation (rotate and zoom). (A) Neuron morphology reconstruction. (B) Neuron presentation after zoom up to 12 times. (C) Neuron presentation after rotate and zoom up to three times.

Figure 11

Comparison of neuron reconstruction model produced (A) without Gouroud shading and (B) with Gouraud shading (Note the effects at the neuron segments connections).

Figure 12

Survey on users’ experience in manipulating neuron data and system.

Figure 13

Individual perception of the necessity for neuron visualization by percentages.

Figure 14

User's feedback on advantage or strengths obtained from prototype development. (1) Manipulation functions for viewing neuron model from different angles. (2) Different colour presentation for differentiating connected segments of neuron model. (3) Focus on specific neuron data for visualization and comparison i.e. usage of neuron SWC data.

Figure 15

Overall feedback on performance achievement of the proposed model.