BLOOD VESSELS SEGMENTATION METHOD FOR RETINAL FUNDUS IMAGES BASED ON ADAPTIVE PRINCIPAL CURVATURE AND IMAGE DERIVATIVE OPERATORS
Diabetes is a common disease in the modern life. According to WHO’s data, in 2018, there were 8.3% of adult population had diabetes. Many countries over the world have spent a lot of finance, force to treat this disease. One of the most dangerous complications that diabetes can cause is the blood vessel lesion. It can happen on organs, limbs, eyes, etc. In this paper, we propose an adaptive principal curvature and three blood vessels segmentation methods for retinal fundus images based on the adaptive principal curvature and images derivatives: the central difference, the Sobel operator and the Prewitt operator. These methods are useful to assess the lesion level of blood vessels of eyes to let doctors specify the suitable treatment regimen. It also can be extended to apply for the blood vessels segmentation of other organs, other parts of a human body. In experiments, we handle proposed methods and compare their segmentation results based on a dataset – DRIVE. Segmentation quality assessments are computed on the Sorensen-Dice similarity, the Jaccard similarity and the contour matching score with the given ground truth that were segmented manually by a human.