Abstract
Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.
Highlights
Retinal vessel segmentation is an image processing procedure that can help in the detection of numerous eye diseases [1]
Because the intensity profile of the vessel is symmetrical in relation to a line passing in the center of the vessels and the vessels are present in the image in several orientations [10], we propose a new kernel function to take into account the vessels orientation
The proposed segmentation method using a combination of filters is tested with two public retinal image databases: Drive [3] and Stare [7]
Summary
Retinal vessel segmentation is an image processing procedure that can help in the detection of numerous eye diseases [1]. Manual tracing of retinal vessels is one method that can be used for segmentation. It is a long and tedious task which requires training and is prone to interoperator variability [2]. The retinal images present two vascular networks: the arterial and the venous. These vessels cross and overlap with some frequency, especially next to the optical disc, hindering the automatic segmentation of the image. For these reasons, retinal vessel segmentation still poses a great challenge
Sign-in/Register to view highlights & summary 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.
