Visualization of 3D neuronal networks

The study of computer graphics is now popular in generating 2D or 3D images. The implementation of computer graphics is essential and the applications are created to be used in various fields such as education and training, biology, architecture, entertainment, visualization, etc. One of the rese...

Full description

Saved in:
Bibliographic Details
Main Author: Shao, LuJie
Other Authors: Zheng Jianmin
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/158997
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-158997
record_format dspace
spelling sg-ntu-dr.10356-1589972022-06-09T00:11:46Z Visualization of 3D neuronal networks Shao, LuJie Zheng Jianmin School of Computer Science and Engineering ASJMZheng@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Computer graphics The study of computer graphics is now popular in generating 2D or 3D images. The implementation of computer graphics is essential and the applications are created to be used in various fields such as education and training, biology, architecture, entertainment, visualization, etc. One of the research fields is about understanding and analysing complicated features and behaviours of the human brain. 3D visualization provides a useful tool for analysing the complicated features and behaviours of dense neural networks. The purpose of the project is to investigate techniques for visualizing the point-and-diameterbased neurons as 3 Dimensional by using a POV-Ray-based visualizer according to the neuronal morphology descriptions defined in neuronal morphology SWC format files. A program is created to convert the existing SWC file of the neuron to a POV-Ray file to render the 3D visualization of the neuron. Bachelor of Engineering (Computer Engineering) 2022-06-09T00:11:46Z 2022-06-09T00:11:46Z 2022 Final Year Project (FYP) Shao, L. (2022). Visualization of 3D neuronal networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158997 https://hdl.handle.net/10356/158997 en PSCSE20-0093 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Computer graphics
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Computer graphics
Shao, LuJie
Visualization of 3D neuronal networks
description The study of computer graphics is now popular in generating 2D or 3D images. The implementation of computer graphics is essential and the applications are created to be used in various fields such as education and training, biology, architecture, entertainment, visualization, etc. One of the research fields is about understanding and analysing complicated features and behaviours of the human brain. 3D visualization provides a useful tool for analysing the complicated features and behaviours of dense neural networks. The purpose of the project is to investigate techniques for visualizing the point-and-diameterbased neurons as 3 Dimensional by using a POV-Ray-based visualizer according to the neuronal morphology descriptions defined in neuronal morphology SWC format files. A program is created to convert the existing SWC file of the neuron to a POV-Ray file to render the 3D visualization of the neuron.
author2 Zheng Jianmin
author_facet Zheng Jianmin
Shao, LuJie
format Final Year Project
author Shao, LuJie
author_sort Shao, LuJie
title Visualization of 3D neuronal networks
title_short Visualization of 3D neuronal networks
title_full Visualization of 3D neuronal networks
title_fullStr Visualization of 3D neuronal networks
title_full_unstemmed Visualization of 3D neuronal networks
title_sort visualization of 3d neuronal networks
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158997
_version_ 1735491110058328064