Retinal vessel segmentation approach based on corrected morphological transformation and fractal dimension

Abstract

The morphological structure of retinal vessels plays an important role in analysing and diagnosing fundus disease. In this study, an unsupervised automatic segmentation method for retinal blood vessels with corrected morphological transformation and fractal dimension is proposed. To enhance the contrast between retinal vessels and background in a fundus image, the morphological operator with linear structural elements is used; to remove the lesion and its light reflection, a compensation method is proposed; to extract the objects from a grey image, the multi-threshold approach is applied; to recognise the blood vessels and noise from the fundus image, a new method based on fractal dimension is presented. The new approach is tested in detail on three public databases STARE, DRIVE and HRF. Experimental results show that the segmentation algorithm is better than other existing unsupervised automatic segmentation algorithms, and the new approach is robust.