Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy.
Medical image analysis is a very popular research area these days in which digital images are analyzed for the diagnosis and screening of different 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 a patient's vision and can also help the ophthalmologists in screening of DR. The background or nonproliferative DR contains four types of lesions, i.e., microaneurysms, hemorrhages, hard exudates, and soft exudates. This paper presents a method for detection and classification of exudates in colored retinal images. We present a novel technique that uses filter banks to extract the candidate regions for possible exudates. It eliminates the spurious exudate regions by removing the optic disc region. Then it applies a Bayesian classifier as a combination of Gaussian functions to detect exudate and nonexudate regions. The proposed system is evaluated and tested on publicly available retinal image databases using performance parameters such as sensitivity, specificity, and accuracy. We further compare our system with already proposed and published methods to show the validity of the proposed system.
Computer Engineering Department, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi 46000, Pakistan. usmakram@gmail.com
M Usman
MU
Akram
Anam
A
Tariq
M Almas
MA
Anjum
M Younus
MY
Javed
D000465:Algorithms; D001499:Bayes Theorem; D003930:Diabetic Retinopathy / Q000175:diagnosis* / Q000473:pathology; D005122:Exudates and Transudates / Q000737:chemistry*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology; D012680:Sensitivity and Specificity