# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Machine Learning Techniques  
While pattern recognition has its roots in engineering, machine learning was developed in computer science [120]. Pattern recognition has become a more well-known and active research field since the 1960s [121], and has undergone considerable development for many years, which yields a variety of paradigms and methods that have important applicability in many fields; retinal vascular structure is such a one. Machine learning algorithms are typically divided into three major categories: _supervised_, _unsupervised_ and _reinforcement learning_. This categorization is mostly based on the availability of responses \(y\) for input data \(x\). Supervised learning addresses the problems where, for each input \(x\), there is a corresponding observed output \(y\), whereas, in the case of the latter two categories, this correspondence cannot be found due to the lack of data. Unsupervised learning explores interesting patterns in the input data without a need for explicit supervision [122]. Reinforcement learning assumes that the dynamics of the system under consideration follows a particular class of model [123]. A back propagation artificial neural network vessel classifier was designed by [PERSON] and [PERSON] [79] to identify the blood vasculature structure in angiogram images. The proposed technique uses the raw grey-intensity values of pixels instead of feature extraction. Each pixel in the image is processed by creating a window around it that covers number of pixels, and then, the raw grey intensities of these pixels are fed into the neural network as input. To cover the entire image, a process of sliding windows, pixel by pixel, takes place. Training dataset consists of manually selected patch samples of angiogram, where the distribution of vessel and background pixels is kept roughly equal to prevent neural network biasing towards background pixels' classification. The proposed method avoids the complexity of feature extraction. Besides, the method achieved vessel detection performance of 92% on angiograms. Transfer learning and domain adaptation [124] has been investigated in the field of retinal vessels segmentation by [86], where a denoised stacked auto-encoder neural network was trained with ample labeled mini-patches of retinal images taken from DRIVE dataset. DRIVE dataset represents the source domain where the auto-encoder was adapted to deploy on STARE dataset which represent target domain. The stacked auto encoder consists of two encoding layers with 400 and 100 nodes per layer, respectively, and is followed by a SoftMax regression layer. Due to power of knowledge transfer, the proposed technique exhibited an accelerated learning performance with area under ROC curve of 0.92. For an exhaustive detection of fine retinal vessels, [PERSON] et al. [84] designed a hybrid framework of deep and ensemble learning, where a Deep Neural Network (DNN) was used for unsupervised learning of vesselness via denoising auto-encoder, utilizing sparse trained retinal vascular patches. The learned representation of retinal vasculature patches was used as weights in the deep neural network, and the response of deep neural network was used in supervised learning process with a random forest for sake of vasculature tissues identification. The high capability of denoising auto encoder to learn feature representation was furiously exploited. The method was trained and tested via DRIVE dataset. Although the achieved average accuracy was 0.9327, it is considered marginally weak in contrast with state of the art approaches; the performance consistency of the method and the ability to identify both coarse and fine retinal vascular structures are considered its unique ambience. Invigorated by the success of [84; 86], [PERSON] et al. [87] presented an ensemble of two parallel levels of stacked denoised auto-encoder networks. Each kernel is accountable for distinguishing a particular orientation of vessel.