# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 3 Retinal Image Processing  
The ocularists scan the retina of patients using fundus camera with high resolution. Accordingly, the situation of retina blood vessels is probed to diagnose retinal diseases. In many cases, it is found that the retinal vascular structure has low contrast with regard to their background. Thus, the diagnosis of retinal diseases becomes a hard task, and applying a suitable image segmentation technique becomes a must for highly accurate retinal vascular structure detection, since it leads to accurate diagnosis. Retina vessel identification and extraction faces many challenges that may be outlined as follows. Firstly, the retinal vessels' widths take a wide range of color intensity range from less than one pixel up to more than five pixels in the retinal image, as shown in Figure 4, which requires an identification technique with high flexibility.  
Figure 4: Pixel width variation of retinal vessels (in pixels).  
To elaborate this challenge, a snippet of MATLAB(r) code has been developed for sake of grey levels substitution in retinal image; the different grey levels of a raw retinal image have been replaced by color ones, as shown in Figure 5. It can be noted that many retinal vessels, either large or tiny ones, take the same background color intensities. This reveals the broad range of colors that may be taken by the retinal vasculature structure, making the identification process more complicated rather than that found in other identification problems.  
This challenge opens the room for a field of research specialized in detecting and segmenting thin (filamentary) retinal vascular structures, as in [18; 19; 20; 21; 22; 23; 24; 25]. Secondly, Vessels identification in pathological retinal images faces a tension between accurate vascular structure extraction and false responses near pathologies (such as hard and soft exudates, hemorrhages, microaneurys and cotton wool spots) and other nonvascular structures (such as optic disc and fovea region). The retinal blood vasculature is a tree-like structure that disperses across the fundus image surface including pathologies. Thin and filamentary retinal vessels melt in the retinal abnormal regions burden the task of accurate vessel segmentation, as shown in Figure 6.  
In summary, retinal vascular structure, inside either normal or abnormal retina images, has low contrast with respect to the retinal background. Conversely, other retinal anatomical structures have high contrast to other background tissues but with indistinct features in comparison with abnormal structures; optic disc and exudates lesions represent typical examples. All these challenges, in terms of  
Figure 5: First challenge of retinal image segmentation: (**a**) Vessels types distribution on retina surfaces; and (**b**) different sizes of retinal vessels.  
Figure 6: Effect of retinal lesions on filamentary vessel structures appearance.  
medical image processing, make the classical segmentation techniques such as Sobel operators [26], Prewitt operators [27], gradient operators [28], and [PERSON] and [PERSON] differential operations [29] inefficient and inaccurate. Consequently, various algorithms and methodologies have been developed and implemented for sake of automatic identification, localization and extraction of retinal anatomical structures and can be broadly divided into rule-based and machine learning techniques, as elaborated in Section 4.