Improvement of Automatic Hemorrhages Detection Methods Using Shapes Recognition
This work addresses early detection of diabetic retinopathy to reduce blindness, but it appears incremental as it builds on existing shape-based methods.
The paper tackled the problem of early automated detection of dot hemorrhages in diabetic retinopathy by introducing a method based on shape recognition, achieving detection and separation of all dot hemorrhages.
Diabetic Retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. The presence of hemorrhages in the retina is the earliest symptom of diabetic retinopathy. The number and shape of hemorrhages is used to indicate the severity of the disease. Early automated hemorrhage detection can help reduce the incidence of blindness. This paper introduced new method depending on the hemorrhage shape to detect the dot hemorrhage (DH), its number, and size at early stage, this can be achieved by reducing the retinal image details. Detection and recognize the DH by following three sequential steps, removing the fovea, removing the vasculature and recognize DH by determining the circularity for all the objects in the image, finally determine the shape factor which is related to DH recognition, this stage strengthens the recognition process. The proposed method recognizes and separates all the DH.