Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings
This Final Year Project (FYP) introduces a smartphone-based, portable polarisation-sensitive eye imaging system for pre-clinical applications. Integrating modern smartphone cameras with polarisation-sensitive optical coherence tomography (PS-OCT), the project aims to enhance ophthalmic diagnostics....
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/175444 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-175444 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1754442024-04-26T15:31:15Z Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings Phang, Matthias Jin Jiat Leopold Schmetterer School of Chemical and Biomedical Engineering Singapore Eye Research Institute (SERI) Singapore Eye Research Institute (SERI) leopold.schmetterer@ntu.edu.sg Engineering This Final Year Project (FYP) introduces a smartphone-based, portable polarisation-sensitive eye imaging system for pre-clinical applications. Integrating modern smartphone cameras with polarisation-sensitive optical coherence tomography (PS-OCT), the project aims to enhance ophthalmic diagnostics. A central feature is the 3D-printed platform, produced using the FlashForge Creator Pro. Despite its precision constraints, the platform effectively stabilises specimens for accurate PS-OCT imaging. Data collection yielded speckle patterns, A-Scans, and B-Scans, processed using MATLAB Python scripts for analysis. Min-max normalisation was applied across various imaging techniques to standardise data, but challenges arose in speckle data analysis due to inherent complexities. Collaboration with research associate involved using TensorFlow in a Neural Net Machine Learning Model to process B-Scans, with a 70/15/15 data split for training, validation, and testing. The SSIM analysis evaluated B-Scan quality before and after Machine Learning processing to assess accuracy. The findings lay groundwork for eye disease detection and management in animal models, and could one day potentially revolutionise early-stage eye disease detection and management for humans. Bachelor's degree 2024-04-24T04:29:46Z 2024-04-24T04:29:46Z 2024 Final Year Project (FYP) Phang, M. J. J. (2024). Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175444 https://hdl.handle.net/10356/175444 en CBE/23/150 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 |
spellingShingle |
Engineering Phang, Matthias Jin Jiat Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings |
description |
This Final Year Project (FYP) introduces a smartphone-based, portable polarisation-sensitive eye imaging system for pre-clinical applications. Integrating modern smartphone cameras with polarisation-sensitive optical coherence tomography (PS-OCT), the project aims to enhance ophthalmic diagnostics.
A central feature is the 3D-printed platform, produced using the FlashForge Creator Pro. Despite its precision constraints, the platform effectively stabilises specimens for accurate PS-OCT imaging. Data collection yielded speckle patterns, A-Scans, and B-Scans, processed using MATLAB Python scripts for analysis. Min-max normalisation was applied across various imaging techniques to standardise data, but challenges arose in speckle data analysis due to inherent complexities.
Collaboration with research associate involved using TensorFlow in a Neural Net Machine Learning Model to process B-Scans, with a 70/15/15 data split for training, validation, and testing. The SSIM analysis evaluated B-Scan quality before and after Machine Learning processing to assess accuracy.
The findings lay groundwork for eye disease detection and management in animal models, and could one day potentially revolutionise early-stage eye disease detection and management for humans. |
author2 |
Leopold Schmetterer |
author_facet |
Leopold Schmetterer Phang, Matthias Jin Jiat |
format |
Final Year Project |
author |
Phang, Matthias Jin Jiat |
author_sort |
Phang, Matthias Jin Jiat |
title |
Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings |
title_short |
Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings |
title_full |
Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings |
title_fullStr |
Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings |
title_full_unstemmed |
Develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings |
title_sort |
develop smartphone based portable polarisation sensitive eye imaging system in pre-clinical settings |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/175444 |
_version_ |
1800916398448312320 |