# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Adaptive Local Thresholding Techniques  
Thresholding is considered one of the most well-known, plain and direct methodologies to image segmentation in general and to medical image segmentation in particular. Whereas the objects arrangement in the natural scene images looks relatively undistinguishable, the arrangement of objects including the organs and tissues in the medical image is usually more discernible. Therefore, thresholding segmentation techniques are used extensively in the research that involves the medical image segmentation, where different tissues and organs are represented in different grey levels. Typically, thresholding techniques, in their basic framework, search for a global value (level) that optimally maximizes the separation between different classes (different tissues in our case) in the image. The effectiveness of thresholding with a global level manifests if the objects in the image under consideration have well-defined areas and if the grey levels can be congregated around values with minimum interference. Uneven illumination, inferior quality of source material, camera artifacts/distortions, and anatomical objects with multi-classes and hybrid features make the global thresholding for the entire retinal image a major source for segmentation errors. Moreover, since retinal image shows soft transition between different grey levels, uneven illumination or noise distortions, the principal segmentation errors begin to appear due to pixel-wise approach that adopted by global thresholding, namely, the pixels that have same grey levels (pixel intensity) will be segmented into the same anatomical object, which is considered a long-standing issue of global thresholding with a singlehard value. To resolve these issues, region-wise thresholding methodologies have been suggested for case of retinal vessels identification, developed and implemented via different techniques which can be classified into three major categories: statistical, knowledge-based and fuzzy-based adaptive thresholding, as shown in Figure 14.  
A novel work by [PERSON] et al. [PERSON] et al. (2016) focused on segmenting small thin vessels through Multi Scale Tensor Voting (MTVF) scheme, based on the fact that small vessels represent nearly 10% of the total surface of vascular network ([PERSON] et al., 2016) which, in turn, represents the framework of statistical-based adaptive thresholding. The proposed technique consists of four major stages: pre-processing; multiscale line detecting vessel enhancement; adaptive thresholding and MTVF processing; and post-processing stage. In the pre-processing stage, the green channel of raw retina image was extracted. Then, image contrast was enhanced by applying a contrast correction approach proposed by ([PERSON] et al., 2016). Dual-tree complex wavelet transform ([PERSON] et al., 2016) was used to remove noise. Following the pre-processing stage, retinal vessels were enhanced via multi-scale line detection approach proposed by ([PERSON] et al., 2016). The output of multi-scale line detector was fed into adaptive thresholding processer to isolate vessels. To obtain various levels of adaptive thresholding, authors have fitted the histogram of MSLD response to a simple Gaussian function and modified the optimum global threshold ([PERSON] et al., 2016) by varying the distance from the mean of Gaussian function through following equation:  
\[T=|\mu_{Gaussian}\mid+\alpha|\sigma_{Gaussian}| \tag{5}\]  
where \(\mu_{Gaussian}\) and \(\sigma_{Gaussian}\) are the mean and standard deviation of the fitted Gaussian function, respectively. Then, various thresholds revealed through experimentally changing \(\alpha\) parameter, which is considered the heart of adaptive thresholding. Once the adaptive thresholding step has been performed, many smaller vessels separate. Thus, a multi-scale tensor voting framework inspired by ([PERSON] et al., 2016; [PERSON] et al., 2016) has been used to reconnect them.