# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques  
Generally, regarding segmentation issue, there exist as many methods and algorithms as there are specific cases and situations. Among them, segmentation tools used for medical purposes in general, and for retinal anatomical structures segmentation in particular. All retinal vessel segmentation methods share common stages: _pre-processing_ stage, _processing_ stage and _post-processing_ stage. In this review, we have categorized the covered papers based on the algorithm or technique used in the processing stage, yielding six major categories: (1) kernel-based techniques; (2) vessel-tracking; (3) mathematical morphology-based; (4) multi-scale; (5) model-based; (6) adaptive local thresholding; and (7) machine learning. These categories are also grouped into two major classifications: _Rule-based_ or _Machine learning_, as shown in Figure 7 and categorized in Table 1.  
The category of rule-based techniques follows specific rules in an algorithmic framework, whereas machine learning ones utilize a pre-segmented retinal image (ground truth or gold standard) to form a labeled dataset that can be used in training process. However, any non-image processing specialist, when faced with an image analysis problem, readily apprehends that a one image transformation or a stand-alone image processing technique usually fails. Therefore, Figure 7 depicts this fact, where nested lobes stand for the _Hybrid Nature_ of these techniques. Most image analysis problems are very complicated, especially medical ones, and can be solved in high performance by a hybrid combination of many elementary transformations and techniques. These reasons give arise to the utilization of hybrid techniques in retinal vessels segmentation problems, as proposed in [30; 31; 32; 33; 34; 35; 36]. The capability of retinal segmentation algorithm to extract the retinal vasculature structure is evaluated by many metrics. The most common ones are: average True Positive Rate (TPR), average False Positive Rate (FPR), average _Sensitivity (recall, TPR)_, average _Specificity (1-FPR)_, average _Accuracy_, and average _Precision_. Sensitivity and specificity represent the most widely used metrics in medical  
Figure 7: Retinal vessels segmentation techniques. research; the higher the specificity and sensitivity values, the better diagnosis. The sensitivity reflects the capability of the algorithm to detect the vessels' pixels, whereas the specificity determines the ability of the algorithm to detect non-vessel pixels. Sensitivity and Specificity represent the features of the algorithm and are associated with the accuracy metric in many medical image processing fields, including retinal vessel segmentation [37], as given by the following equations [38]:  
\[Sensitivity\ (Recall)\ =TP/(TP+FN) \tag{1}\]  
\[Specificity=TN/(TN+FP) \tag{2}\]  
\[Accuracy=(TP+TN)/(TP+FN+FP+TN) \tag{3}\]  
\[Precision=TP/(TP+FP) \tag{4}\]  
where \(TP\) is True Positives, \(FP\) is False Positives, \(FN\) is False Negatives, and \(TN\) is True Negatives. On the other hand, many papers use the area under the Receiver Operating Characteristic (ROC) curve [39; 40] to evaluate their works, especially for methods that highly depend on specific parameters during the segmentation execution. ROC curve is a non-linear function between TPR and FPR values. Optimal area under ROC is 1 for an optimal performance. Most of retinal vessels segmentation techniques and algorithms use the most popular datasets in this field: (1) Digital Retinal Image for Vessel Extraction (DRIVE) [41; 42]; and (2) Structuring Analysis of the Retina (STARE) [43]. Both datasets are well-considered and popular in the field of retinal vessels segmentation to the extent that almost every research performance involving vessels segmentation is evaluated via these datasets. The popularity of these datasets is due to the good resolution of the retinal fundus images and to the availability of manually labeled ground truth images prepared by two experts.