# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Kernel-Based Techniques  
This type of retinal vessels segmentation depends on the intensities distribution of vessel pixels to build a filter kernel, which, in turn, can detect the retinal vasculature structure boundaries. The kernel can either follow a pre-specified form based on the cross-section profile of retinal vessel, or it can be deformable according to vessels boundaries especially when they lie in or in neighbor of hemorrhages and microaneurys lesions. Most often, kernel-based approaches are used as preprocessing image enhancement step for other retinal vessels segmentation methodologies, since it enhances the map for vessels boundaries. The profile-based kernels use one of various models that have been proposed and implemented in retinal vessels profiling that are built based on the idea that intensity distribution of retinal vessel can describe retinal vessels characteristics which can be turned into maps for vessels detection. The basic idea of kernel-based techniques (_also called matched filtering-based_) techniques is to compare the pixels' intensity variations along with the cross-section profile of the retinal vessel with a prefigured template works as a kernel. Therefore, most typical matched filter-based techniques detect retinal vessels by applying a matched filter kernel on the original grey retinal image followed by a thresholding step. Retinal vessel profiling has many applications in the fields of vascular width measurement [92] or in the field of vessels type classification [93]. In the case of vessels detection and extraction, it is used to create the map for process of detection, which paves the way for vessels extraction through region growing or filtering based approaches. Generally speaking, retinal vascular matched kernels can fall in one of two major categories: Gaussian shaped or non-Gaussian shaped [94]. Early work in this direction was performed by [PERSON] et al. [48], who observed the high similarity of the intensity variations of the cross-section profile of the retinal image with a Gaussian function, as illustrated in Figure 8.  
Since [PERSON] et al. [48] published his well-known paper that stated the cross section profile of retinal vascular structure has approximate _Gaussian_ shape, matched filters with Gaussian kernels have emerged, and were later reported in the literature for retinal vessel tree detection. According to the fact that the cross section of retinal vessels can be modeled as a Gaussian function, a series of Gaussian shaped filters (different in Gaussian parameters values \(\upmu\) and \(\sigma\)) can be used to match different vessel sizes simply and efficiently. However, matched filters have strong response to both  
Figure 8: Cross-section intensity profile of the region marked by a straight line between point A and point B on retina image. vessels and non-vessels structures, such as red lesions and bright blobs, which results in degradation in the performance in terms of false detection rate. Three important aspects should be taken into consideration through designing a matched filter kernel: (1) limited curvature of retina vascular structure, where the curvature of vessel segments can be approximated by bell-shape piecewise linear segments; (2) vessels' width, as the width of the retinal vessels decreases in a gradual way when one moves from optical disk towards Fovea region, as shown in Figure 2; and (3) accurate cross-section profile of pixel intensities distribution of the retinal blood vessels [58]. The same idea of [PERSON] et al. [48] was followed and re-implemented by [56] via DRIVE dataset. The regenerated segmentation results have reported an average accuracy of 0.9387, and 0.9647 and 0.6721 for average specificity and average sensitivity, respectively. [PERSON] and [PERSON] [53] proposed a profile kernel-based algorithm for retinal vessel extraction based on profiling the cross-section of retinal vessels using piece-wise Gaussian scaled model.