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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
