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....

Full description

Saved in:
Bibliographic Details
Main Author: Phang, Matthias Jin Jiat
Other Authors: Leopold Schmetterer
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