An automated system for the grading of diabetic maculopathy in fundus images

Authors:
Muhammad Usman Akram;Mahmood Akhtar;M. Younus Javed
Affiliations:
Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Pakistan;Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Pakistan;Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Pakistan
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Year:
2012

Citing 4
Cited 0

Digital Image Processing

Digital Image Processing
A Bayesian network classifier and hierarchical Gabor features for handwritten numeral recognition

Pattern Recognition Letters
Virtual Excursions for Tiny Fingers: A Shared Experience

ICISA '11 Proceedings of the 2011 International Conference on Information Science and Applications
Retinal images: optic disk localization and detection

ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Computer aided diagnosis systems are very popular now days as they assist doctors in early detection of the disease. Diabetic maculopathy is one such disease which affects the retina of the diabetic patients. It affects the central vision of the person and causes blindness in severe cases. In this paper, an automated system for the grading of diabetic maculopathy has been developed, that will assist the ophthalmologists in early detection of the disease. Here, we propose a novel computerized method for the grading of diabetic maculopathy in fundus images. Our proposed system comprises of preprocessing of retinal image followed by macula and exudate regions detection. This is followed by feature extractor module for the formulation of feature set. SVM classifier is then used to grade the diabetic maculopathy. The publicly available fundus image database MESSIDOR has been used for the validation of our algorithm. The results of our proposed system have been compared with other methods in the literature in terms of sensitivity and specificity. Our system gives higher values of sensitivity and specificity as compared to others on the same database.