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Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed. [ABSTRACT FROM AUTHOR]

To develop an automated diabetic retinopathy screening system, a detection of lesions in digital fundus photographs is needed. Microaneurysms are the first clinical sign of diabetic retinopathy. The number of microaneurysms is used to indicate the severity of the disease. Early microaneurysm detection can help reduce the incidence of blindness. This paper investigates a set of optimally adjusted morphological operators used for microaneurysm detection on non-dilated pupil and low-contrast retinal images. The detected microaneurysms are validated by comparing with ophthalmologists' hand-drawn ground-truth. As a result, the sensitivity, specificity, precision and accuracy were 81.61, 99.99, 63.76 and 99.98%, respectively. [ABSTRACT FROM AUTHOR]

Objective: Automatically detect the structure of blood vessels in ROP infants to allow ophthalmologist to analyze and detect the symptom early., Material and Method: This study presents a set of methods for detection of the skeletonized structure of premature infant's low-contrast retinal blood vessel network. Steps has been optimized for this study, namely statistically optimized LOG edge detection filter, Otsu thresholding, Medial Axis transform skeletonization, pruning, and edge thinning., Results: A set of 100 test images are grouped together into five testing groups based on their similar characteristics and clinicians suggestions. The authors applied the series of methods proposed on all the 100 images. The result from the algorithm was compared with ophthalmologists' hand-drawn ground truth and it can detect the blood vessel with a high specificity of 0.9879 and sensitivity of 0.8935., Conclusion: The authors' algorithm can detect blood vessels effectively even though the image quality may not be good, have high noise, and low contrast. The algorithm can also detect the blood vessel at important locations such as the edge of the retina.