# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 5 Discussion and Conclusions  
The existing retinal blood vessel segmentation methodologies are categorized, and described in the last section. We discussed various techniques used in these methodologies for the segmentation of blood vessels in retinal image and compare performance result of the methods. These methodologies were evaluated using publicly available datasets. Various retinal vessels segmentation methodologies follow similar procedures: each methodology initiates by _pre-processing_ step, where the green layer (or grey) is extracted from the raw color retinal image, and then the contrast of the image is enhanced. _Processing_ step represents the heart of algorithm, where the different techniques categorized in last section are used. Finally, in the _post-processing_ step, the initial segmented image undergoes steps of smoothing and edge preserving and enhancement.  
Regarding retinal segmentation categories shown in Figure 7, there is neither a best technique or algorithm to face all performance metrics in high segmentation achievement, nor a best mathematical scheme to do so. Deciding whether the methodology is best depends on a set of factors including: (1) _Achieved accuracy_, which in turn, depends on the achieved specificity and sensitivity, where segmentation is considered the best if it achieves the highest possible sensitivity value (or shows low false detection to other retinal structures), while also maintaining the specificity at optimal level.  
On the other hand, the optimality of the method increases as detection capability of the method records high performance in pathological retinal images; (2) _Time and computational complexity:_ The time and computational power required by the methods tend to be low, as the accuracy has increased tendency on the condition that high performance of high accuracy has achieved; (3) _Robustness_: The method is considered to be best if it shows robustness against method parameters variation.  
The accurate detection and segmentation of the retinal vascular structure forms the backbone of a variety of automated computer aided systems for screening and diagnosis of ophthalmologic and cardiovascular diseases. Even though many promising methodologies have been developed and implemented, there is still room for research improvement in blood vessel segmentation methodologies, especially for noisy and pathological retinal images outside the limited number of retinal images available in public datasets. In real-life applications, retinal vessels segmentation systems will not replace the experts' role in diagnosis; rather, they will enhance the diagnosis accuracy and reduce the workload of the ophthalmologists. Therefore, large volume of patients' images can be processed with high diagnosis accuracy and comparable time.  
The work has been primarily conducted by [PERSON] under the supervision of [PERSON]. [PERSON] provided insightful suggestions to improve this work. Extensive discussions about the algorithms and techniques presented in this paper were had among the three authors over the past year.  
The authors declare no conflict of interest.