Date of Award


Document Type

Masters Thesis

Degree Name


Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science

First Advisor

Roger E. Salters, Ph.D.

Second Advisor

Kimon Valavanis

Third Advisor

James Fogleman


Optical Coherence Tomography, OCT, Glaucoma, Information theory


Glaucoma is a group of optic nerve disease with progressive structural changes leading to loss of visual function. A careful examination and detection of changes in the optic nerve is the key to early diagnosis of glaucoma. Optical Coherence Tomography (OCT) is one of the known techniques of diagnosis of glaucoma. The patients' eyes are scanned and sub-surface images are captured from optical nerves. Captured OCT images usually suffer from noise and therefore image enhancement techniques can help doctors in better analysis of OCT images and diagnosis of glaucoma. In this thesis, we propose three successful algorithms for enhancing the quality and the contrast of OCT images. Our experiments on sample OCT images show that our algorithms can remove noise and disturbance in images and significantly enhance the visual quality of the glaucoma images. Information theory is widely used in image processing these years. It is proved that information theory is very useful to show the trends between the systems. By using information theory, the ability of each algorithm in enhancing the quality of OCT images is examined. Information theory helped us to find out the relationships of the algorithms. In this research, we use sequential images taken in different time of a same patient and compare the health level of them with the help of Information theory. Information theory successfully helped to provide trends among the sequential images, which will help doctors to diagnosis.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Shuting Wang


Received from ProQuest

File Format




File Size

127 p.


Electrical engineering