ORCID Identifier(s)

0000-0002-1688-3798

Graduation Semester and Year

2020

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Vassilis Athitsos

Abstract

Glaucoma is the second leading cause of blindness all over the world, with apparently 75 million cases reported worldwide in 2018. If it’s not diagnosed at an early stage, glaucoma may cause irreversible damage to the optic nerve which results in blindness. The Optic head examination is the widely used structured diagnosis approach in the current medical field for Glaucoma detection which involves measuring the Optic Cup-to-Disc ratio from the fundus image. Estimation of Optic Cup-to-Disc requires accurate segmentation of the Optic Cup and Optic Disc from the fundus which is a tedious and time-consuming task even for the experienced ophthalmologist. This thesis addresses the challenge by using the Residual blocks and deep learning segmentation network (Encode-Decoder Network) to form a model called Modified Residual U-Net Convolutional Neural Network (Res U-Net) for automatic segmentation of Optic Cup and Optic Disc. Our experiments include the comparison of various methods on the publicly available dataset like DRIONS-DB and RIMONE V3. For Optic Cup and Optic Disc segmentation, my method performs competitively compared to the other techniques in terms of quality of recognition.

Keywords

Optic cup-to-disc segmentation, Modified res u-net, Deep learning segmentation network, Glaucoma detection, Fundus image

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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