# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Vessel Tracking/Tracing Techniques  
The heart of vessel tracking algorithms is to trace the ridges of retina fundus image based on a set of starting points. Graphical representation of ridges of retina image can be noticed in Figure 11. Any tracking algorithm involves seeds selection as a preliminary step, where seeds can be defined either manually or automatically. The ridges of vessels are detected by inspecting zero-crossing of the gradient and curvature. However, \"clean-limbed\" ridges detection need a pre-processing phase involves complicated steps of vessel enhancement for all vessels sizes and orientations. Consequently, one of the major drawbacks of vessel tracking is the extreme dependency on the pre-processing steps that proceed the phase of tracing. In tracing techniques, it is not an essence for seed points (_starting points of tracking process_) to be located at the center of retinal vessels, [PERSON] et al. [59], for instance, have extracted seed points from the circumference of the optic disc, then the centers of vessels were traced using an extended Kalman filter. A semi-ellipse was defined around the optic disk as a searching region for starting points of vascular structure, which was later used by [65]. As the candidate pixels locations for next vessel edge points were selected on the semi-ellipse, vessel tracking took place based on Bayesian theory. [PERSON] et al. [62] proposed a vessel tracking methodology for retinal vasculature structure extraction that combines Hessian and matched filters and an idea of exploiting the edge information at the vessels parallel boundaries, which as first proposed by [PERSON] and [PERSON] [60] for retinal vessels extraction. Once the contrast between vessels and other retina tissues are enhanced and the information of sizes and the orientations of enhanced vessels are available, the ridges are used to trace vessels via their center lines along ridge seeds that were selected automatically. The tracking performance was tested  
Figure 10: Summarized graphical comparison between some of kernel-based methods performance (_Accuracy, Sensitivity_ and _Specificity_) based on STARE dataset. via DRIVE dataset, where 84.4% of retinal vasculature skeleton was successfully detected with 19.3% false positive rate, and the majority of false tracked vessels were the small ones that the researchers considered a subject of further ongoing research. In contrast to [PERSON] and [PERSON] (2016), [PERSON] and [PERSON] (2016) proposed a tracking methodology trace the retinal vessels centers through Kalman filter, where it has the capability to detect both wide and thin vessels, even in noisy retinal images, by defining a linear model. The proposed model proceeds into four stages. Firstly, a set of seed points all over the image were found by convolving the whole retina image with a set of matched filters at different scales and orientations in aim of finding at least one seed point at each vessel, which, in turn, remove the need to follow all branches. Secondly, Kalman filter was used to trace blood vessels' centers starting from seed points found in the first stage. Thirdly, tracing process ceases once the probability of vessel tracing is small for a number of back-to-back moves or once tracing hit previously segmented vessel. Fourthly, the segmentation results are traced in the case of tracking failure in less than minimum number of steps. The proposed tracking methodology managed to detect retinal vessels with true positive rate reaching up to 85.9% and false negative of 8.1% via DRIVE dataset. Making use of mathematical graph theory, [PERSON] et al. (2016) designed a novel technique to extract the filamentary retinal structure. Their technique was built based on connecting the tracing problem and the digraph matrix-forest theorem in algebraic graph theory with a primary goal to address the vessel cross-over issue. The proposed technique is composed of two main stages.