الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Medical imaging is currently undergoing a rapid development with a strong
emphasis being placed on the use of the imaging technologies to render surgical and
therapeutic procedures to improve the accuracy. Retinal imaging is the technique of
creating visual representations of the interior of the eye. It has a vital part of
ophthalmology practice. It helps in the early detection of vision disorders and
diseases that can affect the eye such as diabetes, hypertension, glaucoma, and
macular degeneration. Diabetic retinopathy (DR) is a condition, where the retina is
damaged due to fluid leaking from the blood vessels into the retina. In extreme
cases, the patient will become blind. Therefore, early detection of diabetic
retinopathy is crucial to prevent blindness. Although retinal imaging is a common
clinical procedure used to determine if a patient suffers from DR, there are several
artifacts that affect retinal images and cause poor quality of them. Images with
camera artifacts can lead to false diagnostics. Artifacts are caused by poor
illumination, degradations coming from blurring. The main objective of this thesis
is to overcome artifacts in fundus images using different denoising techniques and
implement automated detection algorithms to process a large number of fundus
images captured from mass screening of diabetic patients with high accuracy. The
automated detection system is used to detect the retinal features such as blood
vessels area, microaneurysms area, exudates area and texture features. These
features are fed to an artificial neural network (ANN) classifier. The ANN
classifies the data into two categories; normal and abnormal. A proposed hybrid
detection algorithm for automatic feature detection based on entropy and
homogeneity is used to improve the performance of the automated system and
increase the specificity and accuracy. The proposed system is considered as an
automatic tool that can aid ophthalmologists to diagnose and screen diabetic
retinopathy.