The automatic detection of lesions is a fundamental tool for my future research on the progression of diabetic retinopathy. My secondment in Lincoln was aimed at testing and adjusting an existing algorithm available at my partner University. While the algorithm was created to find all types of lesion to automate the diagnosis of referable maculopathy, my future research will need it for the detection of micro-aneurisms.
Testing the algorithm on the pictures from high-risk patients from my previous study showed that more than half of micro-aneurisms were not detected. It was found that many of the lesions were not detected on the candidate-selection stage of the algorithm. A newly developed method, based on the template matching technique, could find 50% more true-lesions candidates.
The combination of the two methods detects approximately 70% of the true lesions among the candidates from funds photographs from high-risk patients.