Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.

ABSTRACT: The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE) method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract t

SUBMITTER: Memari N 

PROVIDER: S-EPMC5724901 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature