Automated detection of microaneurysms by using region growing and fuzzy artmap neural network

Abstract

Objective: To assess whether the methodological changes of this new algorithm improves the results of a previously presented strategy. Methods: We enhance the image and filter out the green channel of the digital color retinog- raphy. Multitolerance thresholding was applied to obtain candidate points and make a seed growing region by varying intensities. We took 15 characteristics from each region to train a fuzzy Artmap neural network using 42 retinal photographs. This network was then applied in the study of 11 good quality retinal photographs included in the diabetic retinopathy early detection screening program, with initial stages of retinopathy, obtained with the Topcon NW200 non-mydriatic retinal camera. Results: Two experienced ophthalmologists detected 52 microaneurysms in 11 images. The algorithm detected 39 microaneurysms and 3752 more regions, confirming 38 microa- neurysm and 135 false positives. The sensitivity is improved compared to the previous algorithm, from 60.53% to 73.08%. False positives have dropped from 41.8 to 12.27 per image. Conclusions: The new algorithm is better than the previous one, but there is still room for improvement, especially in the initial determination of seeds