Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Applied Mathematics | en_US |
| dc.contributor.advisor | Yiu, Ka-fai Cedric (AMA) | en_US |
| dc.creator | Man, Nora | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13183 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | On feature extraction techniques for eye images | en_US |
| dcterms.abstract | Optical Coherence Tomography (OCT) is a non-invasive method which can capture high-definition images of cross-section of the retina. Based on the thickness of different layers of the retina in OCT images, one can diagnose ocular diseases in an early stage. Numerous algorithms, including deep learning methods, have been used by various researchers on OCT images for retinal layer segmentation of normal eyes and eyes with ocular diseases. Performances of deep learning algorithms have been compared with the state-of-the-art algorithm/software at the time of research. In this thesis, retinal segmentation of OCT images is carried out for nine boundaries, equivalent to segmenting eight retinal layers. A thresholding optimization method using gradient filter is first proposed for the segmentation problem. Different neural network architecture combining U-net and other CNNs have also been investigated. To reduce network complexity, a method is proposed based on the concept of domain decomposition when training a large volume of data on cloud platform. At last, 3D CNN classification model and 3D U-net segmentation model are proposed and trained to classify an eye as healthy or glaucomatous and segment the eight retinal layers using 3D volumetric OCT eye scans. | en_US |
| dcterms.extent | 121 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Retina -- Tomography | en_US |
| dcterms.LCSH | Diagnostic imaging -- Mathematics | en_US |
| dcterms.LCSH | Image processing -- Digital techniques | en_US |
| dcterms.LCSH | Eye -- Tomography | en_US |
| dcterms.LCSH | Optical coherence tomography | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/13183