A Computationally Efficient Red-Lesion Extraction Method for Retinal Fundus Images

Retina is an important organ of the body, the diseases of which may lead to serious damages to human vision. Fundus retinal images are the common tools for the analysis of diabetic retinopathy (DR), which is an important retinal disease. Red-lesions are from important manifestations of DR in the fundus images. In this article, a novel method is suggested for the extraction of red-lesions from fundus images. This method can detect red-lesions without the need for prior segmentation of blood vessels or lesions. The new method works based on dividing the fundus image into square patches and finding the dark ones based on the percentage of dark pixels. After finding dark patches, it is necessary to discriminate the patches that belong to the blood vessel and red-lesion. The continuing structure of blood vessels is considered a discriminating factor for the mentioned purpose. To mathematically model the continuing structure, several states are considered for the way of locating dark patches in a neighborhood. The formation of the blood vessel in vertical, horizontal, and diagonal directions is modeled in the different states. Also, the conditions of the formation of red-lesion in each direction are declared. The performance of the proposed method is evaluated on several datasets. The simplicity of computations, high speed, and acceptable accuracy are significant advantages of this method. The proposed method is capable of providing 92% and 88% for sensitivity (SE) and specificity (SP) in the Diaretdb1 dataset. Also, it provides the values of 91% and 89% for SE and SP in the Diaretdb0 dataset. Furthermore, the SE and SP values for the FIRE dataset are equal to 90% and 92%, respectively.