Examining In Vivo Changes in Lamina Cribrosa in Non-human Primates with Experimental Glaucoma
| dc.contributor.advisor | Zouridakis, George | |
| dc.contributor.advisor | Porter, Jason | |
| dc.contributor.committeeMember | Vilalta, Ricardo | |
| dc.contributor.committeeMember | Tsekos, Nikolaos V. | |
| dc.contributor.committeeMember | Subhlok, Jaspal | |
| dc.creator | Sredar, Nripun 1983- | |
| dc.creator.orcid | 0000-0002-9567-9815 | |
| dc.date.accessioned | 2017-07-21T20:30:54Z | |
| dc.date.available | 2017-07-21T20:30:54Z | |
| dc.date.created | May 2015 | |
| dc.date.issued | 2015-05 | |
| dc.date.submitted | May 2015 | |
| dc.date.updated | 2017-07-21T20:30:55Z | |
| dc.description.abstract | Glaucoma is a disease that results in the degeneration of retinal ganglion cell axons and the death of retinal ganglion cells (RGCs). It is one of the leading causes of permanent blindness worldwide. Clinical examinations currently in practice are limited in their ability to detect glaucoma prior to loss of RGC axons. The main goal of this work is to characterize early changes in the optic nerve head of monkeys with experimental glaucoma (EG) using in vivo and non-invasive methods to better understand the mechanisms behind glaucoma. In vivo images of the lamina cribrosa were acquired using a spectral domain optical coherence tomography and an adaptive optics scanning laser ophthalmoscope (AOSLO). We transformed 2D AOSLO images onto a 3D anterior lamina cribrosa surface (ALCS) and computed the 3D morphometry of the ALCS. Using principal component analysis (PCA), we estimated the predominant local ALCS beam orientation directly from raw grayscale in vivo images without the need for binary segmentation. Subsequently, we developed an automated method to segment the lamina cribrosa pores using level sets. Our 3D transformation method provides a better representation of the ALCS from in vivo images. Following 3D transformation, mean pore area increased by 5.1 ± 2.0% in 11 normal eyes and 16.2 ± 5.9% in 4 glaucomatous eyes due to the increased curvatures. Our PCA technique yielded small errors in local orientation (0.2 ± 0.2◦) when tested on synthetic data, accurately determined local beam orientation and was repeatable in control eyes over time. In addition, automated segmentation of pore boundaries using level sets method was comparable to manual segmentation (sensitivity = 83%, specificity = 95%) and yielded repeatable values over time. In conclusion, the PCA beam orientation and level sets segmentation methods can be used to accurately and objectively detect and track in vivo changes in lamina cribrosa microarchitecture during the progression of EG. | |
| dc.description.department | Computer Science, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Sredar, Nripun, Kevin M. Ivers, Hope M. Queener, George Zouridakis, and Jason Porter. "3D modeling to characterize lamina cribrosa surface and pore geometries using in vivo images from normal and glaucomatous eyes." Biomedical optics express 4, no. 7 (2013): 1153-1165. https://doi.org/10.1364/BOE.4.001153 | |
| dc.identifier.uri | http://hdl.handle.net/10657/1924 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Glaucoma | |
| dc.subject | Image processing | |
| dc.subject | Segmentation | |
| dc.subject | Adaptive optics | |
| dc.title | Examining In Vivo Changes in Lamina Cribrosa in Non-human Primates with Experimental Glaucoma | |
| dc.type.dcmi | text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science, Department of | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |