Abstract:
Medical image analysis is very popular research area these days in which digital images are
analyzed for the diagnosis and screening of di erent medical problems. Diabetic retinopathy
(DR) is an eye disease caused by the increase of insulin in blood and may cause blindness.
An automated system for early detection of DR can save patient's vision and can also
help the ophthalmologists in screening of DR. In this project, we develop algorithms for
detection of DR and its di erent lesions such as Microaneurysms (MAs), Haemorrhage (H),
Hard Exudates (HE) and Cotton wool spots (CWS). The project consists of three stages i.e.
preprocessing, feature extraction and nally the classi cation. In preprocessing, it segments
out the background, noise, blood vessels and optic disk (OD). We use Gabor lter bank
for extraction of candidate lesions and then we form feature set for each lesion consisting
of color, gray level, shape and statistical features. Finally, the classi er takes the feature
sets as input and classi es them as MAs, H, HE or CWS. The implemented algorithms are
tested on publicly available databases of retinal images and are evaluated using performance
parameters such as sensitivity, speci city and accuracy. We have compared our algorithms
with already proposed and published methods. This project will help the ophthalmologists
in saving their time and can be applied as an application of tele-medicine system.
