A Local Adaptive Algorithm for Microaneurysms Detection in Digital Fundus Images

Abstract

Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of thresholding based algorithms have been proposed for MAs detection in mass screening. Most of them process fundus images globally without a mechanism to take into account the local properties and changes. Their performance is often susceptible to nonuniform illumination and locations of MAs in different retinal regions. To keep sensitivity at a relatively high level, a low grey value threshold must be applied to the entire image globally, resulting in a much lower specificity in MAs detection. Therefore, post-processing steps, such as, feature extraction and classification, must be followed to improve the specificity at the cost of sensitivity. In order to address this problem, a local adaptive algorithm is proposed for automatic detection of MAs, where multiple subregions of each image are automatically analyzed to adapt to local intensity variation and properties, and furthermore prior structural features and pathology, such as, region and location information of vessel, optic disk and hard exudate are incorporated to further improve the detection accuracy. This algorithm effectively improves the specificity of MAs detection, without sacrificing the achieved sensitivity. It has potential to be used for automatic level-one grading of diabetic retinopathy screening.