# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Kernel-Based Techniques
The response of matched filter that has Gaussian kernel was used to detect vessels, while the local mean of the response of first-order derivative of Gaussian kernel was used to establish and adjust a \"dynamic threshold\", which, in turn, was used in the thresholding phase that followed the matched filter phase. The proposed technique exploits the difference between FDOG kernel responses for both vessels and non-vessels regions (such as bright blobs, lesions, and optic disk) to vary thresholding level according to local mean signal. The experimental results, obtained via both DRIVE and STARE datasets, demonstrated that applying hybrid matched filtering kernels can reduce the false detection dramatically to less than 0.05 compared to that inherently generated with Gaussian kernel, even for thin vessels, with average accuracy of 0.9510 for normal cases retina images and 0.9439 for pathological ones. According to techniques reported and discussed above, most conventional matched filters-based approaches enhance the performance of matched filter-based methodology by enhancing the performance of the thresholding techniques rather than improving the matched filter kernel itself. [PERSON] et al. [55], on the other hand, replaced the Gaussian kernel of matched filter by Caushy Probability Density Function (CPDF), and reported an overall accuracy of 0.9170 with 3.5% false positive rate via DRIVE dataset. The inherent zero-crossing property of Laplacian of Gaussian (LoG) filter was exploited in an algorithm proposed by [PERSON] et al. [57] where two-dimensional matched filters with LoG kernel functions are applied to fundus retinal images to detect retinal vasculature structure which are firstly enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method. The proposed algorithm achieved average accuracy of 0.9626, and sensitivity and specificity of 0.7006 and 0.9871 via DRIVE dataset, respectively, and average accuracy of 0.9637, and 0.7675 and 0.9799 for sensitivity and specificity, respectively, via STARE dataset in comparison with average accuracy of 0.9340 and 0.7060 and 0.9693 for sensitivity and specificity respectively on DRIVE dataset achieved by [PERSON] et al. [47] using improved two dimensional matched filter with two-dimensional Gaussian kernel. The method was applied on STARE dataset as well, where it has achieved an overall accuracy of 0.9341, and 0.7847 and 0.9512 for sensitivity and specificity, respectively. Figure 9: Summarized graphical comparison between some of kernel-based methods performance (_Accuracy_, _Sensitivity_ and _Specificity_) based on DRIVE dataset. As a novel matched filter kernel improvement, [PERSON] and [PERSON] [58] suggested the Gumbel PDF as a kernel function, where they noted the slight skewness of vessel-cross section profile, which most approximates Gumbel PDF with respect to Gaussian and Caushy PDF functions proposed in [48; 50]. In the thresholding phase, entropy-based optimal thresholding was used in a companion of length filtering as post-processing step to remove isolated pixels. The proposed technique showed an improved performance in terms of average detection accuracy of 0.9522 for DRIVE dataset and 0.9270 for STARE dataset, and the value of area under ROC curve was 0.9287 and 0.9140 for DRIVE and STARE datasets, respectively. Since the performance metrics used in reported papers are not common, Figures 9 and 10 illustrate graphical comparisons between some of the reviewed kernel-based methodologies for DRIVE and STARE datasets based on accuracy, sensitivity and specificity metrics.