Analysis of retinal blood vessel segmentation techniques: a systematic survey

Segmentation of Blood Vessel is a challenging mission in medical image processing to diagnose the disease. It evaluates vessels crucial in automatic retinal vessel extraction with different methodologies, techniques and algorithms to predict the diseases such as Laryngology, neurosurgery and ophthalmology. Using a computer-aided technique, segmentation of blood vessels is conducted in the retina closer to the clinical application routine. This research aims to provide an overview of numerous retinal vessel segmentation approaches, analyse different categories of segmentation techniques, provide a brief description, and evaluate the performance measures. It also reviews, examines, and classifies the procedures, techniques, and methodologies and highlights the important points. The main intention is to provide the reader with a framework for the existing research, introduce the range of retinal vessel segmentation procedures, deliberate the current trends and future directions and summarize the open problems. First, retinal image photography is introduced from the fundus camera. Pre-processing operations and methods of identifying retinal vessels on computer-aided techniques are introduced and discussed to validate results based on the evaluation of various segmentation techniques. The performance of various segmentation techniques and algorithms is estimated using a publicly present database such as DRIVE, STARE, HRF, CHASE, Infant and MESSIDOR. The performance and comparison of various algorithms are assessed in average accuracy, sensitivity, specificity and ROC curves. A huge volume of techniques is considered based on retinal vessel segmentation published in current years. A systematic review is constructed by considering the publications from 2001 to 2021, focusing on methods based on automatic vessel segmentation and classification using fundus camera images. The advantages and limitations are discussed, and tables are included for summarising results at-a-glance. Then an attempt is made to measure the quantitative merit of segmentation methods in terms of accuracy development compared to other methods. Finally, it represented the recent trends with the future direction and summarized the open challenge issues.