Automated detection of microaneurysm for fundus images

Chronic hyperglycemia of diabetes may lead to failure in various organs, especially the blood vessels, eyes, kidneys, nerves and heart. It happens due to the faults either in insulin secretion, insulin action, or both. As diabetes developed, the vision of patient starts to deteriorate, leading to Diabetic Retinopathy (DR). Microaneurysm (MA) are the earliest sign of DR where it appears in clusters as tiny, dark red spots or tiny hemorrhages-like within the retina light-sensitive area. Thus, the objectives of this study are to develop an automated algorithm to perform early detection of MA presence in fundus images, and to evaluate the performance of the proposed system design by evaluating the accuracy of the segmented MA. The methods involved in the pre-processing stage are the green component extraction and bottom hat filtering with gamma correction. As the characteristics of blood vessel and MA are the same, the extraction of vessels is needed. This is done by applying the Gaussian matched filter. It is then segmented out by using certain threshold value. In template learning, wavelet coefficient is used in separating the pattern and background image by following the Gaussian distribution curve. Texture energy filter is used to extract the true features where MA are identified. As a result, 84.15% of accuracy is obtained.