Recent Advancements in Retinal Vessel Segmentation

• Published in: Journal of medical systems Vol. 41; no. 4; p. 70
• Main Authors: L Srinidhi, Chetan, Aparna, P, Rajan, Jeny
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2017; Springer Nature B.V
• Subjects: Algorithms; Blood vessels; Eye diseases; Fundus Oculi; Health Informatics; Health Sciences; Humans; Image & Signal Processing; Image processing systems; Image Processing, Computer-Assisted - methods; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Retina; Retinal Diseases - diagnostic imaging; Retinal Diseases - pathology; Retinal Vessels - diagnostic imaging; Retinal Vessels - pathology; Statistics for Life Sciences; Fundus image; Retinal vessel; Review; Segmentation
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-017-0719-2

Retinal vessel segmentation is a key step towards the accurate visualization, diagnosis, early treatment and surgery planning of ocular diseases. For the last two decades, a tremendous amount of research has been dedicated in developing automated methods for segmentation of blood vessels from retinal fundus images. Despite the fact, segmentation of retinal vessels still remains a challenging task due to the presence of abnormalities, varying size and shape of the vessels, non-uniform illumination and anatomical variability between subjects. In this paper, we carry out a systematic review of the most recent advancements in retinal vessel segmentation methods published in last five years. The objectives of this study are as follows: first, we discuss the most crucial preprocessing steps that are involved in accurate segmentation of vessels. Second, we review most recent state-of-the-art retinal vessel segmentation techniques which are classified into different categories based on their main principle. Third, we quantitatively analyse these methods in terms of its sensitivity, specificity, accuracy, area under the curve and discuss newly introduced performance metrics in current literature. Fourth, we discuss the advantages and limitations of the existing segmentation techniques. Finally, we provide an insight into active problems and possible future directions towards building successful computer-aided diagnostic system.