# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Kernel-Based Techniques
Once the profile has been modeled, a phase alignment function based on data obtained from oriented log-Gabor wavelet was applied. After boundary area map of retinal vascular structure has been produced, cross-sections were extracted by following an approach was proposed by same author in [95]. A notable vessel extraction performance has been achieved by [PERSON] et al. [51], where matched filter in a conjugation with entropy-based adaptive thresholding algorithm was employed. The methodology was applied on DRIVE dataset where it used matched filter in sake of piecewise linear segments enhancement of the retina vascular structure. Later, a co-occurrence matrix [96] that records the number of transitions between all pairs of grey-retinal levels was captured where the grey-level changes were depicted. Then, the entropy of the image grey levels distribution was exploited through second-entropy thresholding to segment the background pixels from the foreground (vessels) ones. The time consumed in the process of obtaining vascular structures approximated 3 s, and high detection accuracy was achieved, up to 0.9759, as well as sensitivity and specificity of 0.9648 and 0.9480, respectively. Compared to the performance achieved in [51], [PERSON] and [PERSON] [49] followed same procedure that was proposed in [51] to extract both the retinal vessel and the optic disk using STARE dataset. However, the time consumed approximated 2.5 min per retina image; most of it was consumed in matched filtering and local entropy thresholding steps. Moreover, it required post-processing steps that were not required by [51] including long filtering stages for isolated pixel removal. Then, filtering steps were followed by morphological thinning used to identify the retinal vascular intersection/crossovers. on the other hand, the optic disk identification proceeded into two major stages: (1) optic disk center identification through maximum local variance detection; and (2) optic disk boundary identification through snake active contour. Even though the same methodology steps have been followed by both [49; 51], they are extremely different in terms of achieved performance. [PERSON] et al. [56] have noted the important effect of Gaussian kernel parameters on the subsequent image processing stages. [PERSON] et al. [56] followed same procedure that was proposed in [49; 51] as well. However, the parameters of Gaussian function have been modified in a way that enhances the overall performance, which reached up to 0.9459 for accuracy, and 0.9721 and 0.6735 for specificity and sensitivity, respectively, using DRIVE dataset in comparison with average ROC area of 0.9352 reported by [PERSON] et al. [50] applied on DRIVE dataset, where they used a different set of modifications for Gaussian-kernel parameters. On the same procedure that have been reported in [49; 51; 56], [PERSON] and [PERSON] [54] employed Gabor filter instead of Gaussian one in the early stages of vessel extraction. The enhanced vessels were obtained via banks of 12 different oriented Gabor filters in range of 0 to 170 degree. Gabor-filter based approach outperforms the Gaussian one in terms of both area under ROC curve and in terms of specificity. The overall achieved sensitivity is less than that achieved by [51; 56]. The performance of [51; 56] was evaluated via DRIVE dataset, whereas the performance of [56] was evaluated on both DRIVE and the challengeable (_pathologies bearing_) STARE dataset, where it showed a high specificity of 96% in the presence of lesions in abnormal retinal images. Based on the fact that retinal vessels have symmetric Gaussian cross-section profile, while the cross-section profile for the non-vessels is asymmetric, two matched filters, one constructed with symmetric Gaussian (zero-mean) kernel and the other with first-Order Derivative Of Gaussian (FDOG) kernel, were applied to retina images by [PERSON] et al. [52].