A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends

Abstract

The visual exploration of retinal blood vessels assists ophthalmologists in the diagnoses of different abnormalities of the eyes such as diabetic retinopathy, glaucoma, cardiovascular ailment, high blood pressure, arteriosclerosis, and age-related macular degeneration. The manual inspection of retinal vasculature is an extremely challenging and tedious task for medical experts due to the complex structure of an eye, tiny blood vessels, and variation in vessels width. Several automatic retinal vessels extraction techniques have been proposed in contemporary literature, which assist ophthalmologists in the timely identification of an eye disorders. However, due to the fast evolution of such techniques, a comprehensive survey is needed. This survey presents a comprehensive review of such techniques, strategies, and algorithms presented to date. The techniques are classified into logical groups based on the underlying methodology employed for retinal vessel extraction. The performance of existing techniques is reported on the publicly accessible datasets in term of various performance measures, providing a valuable comparison among the techniques. Thus, this survey presents a valuable resource for researchers working toward automatic extraction of retinal vessels.

An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach

“…In this section, the most recent and relevant vessels segmentation approaches separated in these two categories are briefly discussed. For a recent detailed review of retinal vessel segmentation techniques please refer to [13].…”

A Hybrid Unsupervised Approach for Retinal Vessel Segmentation

“…Numerous methodologies for RVS have been developed in literature [ 4 , 10 ]. These methodologies are arranged into two sets: supervised and unsupervised procedures.…”

“…Some of the techniques are not fully automatic while others are incapable to handle pathological images. Some of these methods are evaluated on the datasets having a limited number of images while others have problems of oversegmentation or undersegmentation with abnormal images [ 10 ]. Hence, the dilemma of perfect RVS is still not answered.…”

Retinal vessel segmentation approach based on corrected morphological transformation and fractal dimension

“…Many scholars have studied the automatic retinal vessel segmentation algorithm, they are generally divided into two categories: supervised algorithm and unsupervised algorithm [3], today, two other categories methods are proposed for segmenting retinal vessel: model‐based techniques, hardware‐based methods [14–16] and hybrid segmentation methods [17–19]. The hardware‐based methods improve in terms of both execution time and power efficiency (6X and 5.7X, respectively) [15], and for the implementation still need software, they cannot improve the segmentation performance.…”