Student:	Uğur ŞEVİK
Supervisor:	Doç. Dr. Cemal KÖSE
Department:	Bilgisayar Mühendisliği
Institution:	Graduate School of Natural and Applied Sciences
University:	Karadeniz Technical University Turkey

Title of the Thesis:	Retinal Image Quality Assessment And Detection Of Diabetic Retinopathy Disease
Level:	Ph.D.
Acceptance Date:	9/6/2014
Number of Pages:	153
Registration Number:	Di1024

Summary:

Diabetic retinopathy (DR) is an eye disease caused by diabetes, and results blindness

if it is not diagnosed at early stages. DR is the major disease that causes blindness in the

world. Patients with diabetes should be monitored using fundus imaging by an

ophthalmologist for early diagnosis and treatment of DR. However, incidence of DR disease

rises considerably depending on increased number of diabetes patients. Adequate amount of

time and number of experts are needed to monitor DR patients. Unfortunately, these

resources is unavailable at sufficient rates. Hence, we proposed automatic methods for

diagnosing DR disease that will reduce diagnosis time of ophthalmologist by eliminating

diagnosis of unnecessary images. In this thesis, a novel method for assessing adequacy of

fundus image quality is proposed at first. After identification of suitable retinal images,

fundamental anatomical structures of retina, which are blood vessels, optic disc and fovea,

are detected. These anatomical structures are crucial for diagnosis of abnormalities in the

retina. Finally, machine learning algorithms are used to identify hard and soft exudates,

hemorrhages and microaneurysms.

Key Words: Retinal image analysis, Image quality assessment, Diabetic retinopathy, Blood

vessels, Optic disc, Fovea, Image classification, Image processing, Machine

learning.