Automated techniques for blood vessels segmentation through fundus retinal images: A review. Issue 2 (5th January 2019)

• Record Type: Journal Article
• Authors: Akbar, Shahzad; Sharif, Muhammad; Akram, Muhammad Usman; Saba, Tanzila; Mahmood, Toqeer; Kolivand, Mahyar
• Is Part Of: Microscopy research and technique. Volume 82:Issue 2(2019)
• Page Start: 153
• Page End: 170
• Publication Date: 2019-01-05
• Subjects: retinal blood vessels -- retinal diseases -- retinal image databases -- supervised techniques -- unsupervised techniques; Electron microscopy -- Technique -- Periodicals; Microscopy -- Periodicals; Microscopy -- Technique -- Periodicals; 502.825
• DOI: 10.1002/jemt.23172
• Languages: English
• ISSNs: 1059-910X

Abstract

Abstract: Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research. Abstract : The current review elaborates analysis of several automatic retinal vessel segmentation methods evaluated on benchmark datasets such as DRIVE and STARE. Results thus obtained so far are compared and future directions are suggested.