Background:
Diabetic retinopathy is one of the leading causes of blindness worldwide. Furthermore, it is considered the most important complication of diabetes mellitus, which creates various lesions in the retina at its different stages. Moreover, these lesions appear in different forms of hemorrhages, exudates, and microaneurysms. The count and type of these lesions can determine the severity and progression of the disease. Early detection of these lesions can lead to better treatment and blindness prevention. The accurate segmentation of these lesions is required to detect them and specify their counts and types. Since the manual segmentation of retinal lesions is tedious and time-consuming, automated segmentation is preferred. Furthermore, in screening programs where a large population needs to be considered, automated segmentation is inevitable. Therefore, automatic segmentation of retinal lesions is the first stage of any typical computer-aided diagnosis system for early diagnosis of the disease. Automated segmentation of retinal lesions is a challenging task due to the shape diversity and inhomogeneity existing in these lesions. Hence, more advanced segmentation techniques capable of modeling lesion complexities are required to tackle difficulties regarding the automated segmentation of diabetic retinopathy lesions in retinal fundus images.
