# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Machine Learning Techniques
The segmentation algorithm was evaluated on the STARE dataset resulting in an average sensitivity and specificity of 77% and 90%, respectively, for three feature vectors, and 76% and 93%, respectively, using only one feature (maximum eigenvalue) vector  
A fuzzy-based retinal vessel segmentation methodology proposed by [PERSON] and [PERSON] [85] used the difference between low-pass filtered and high-pass filtered version of retinal image as input for fuzzy-logic based processing. The fuzzy logic consists of different sets of fuzzy rules; each fuzzy rule was built based on different thresholding values. Thresholding values were used to select and discard pixel values according to fuzzy rules, which, in turn, led to vessel extraction. The methodology attained an average accuracy of 95% on DRIVE dataset. A two-stage combination between vessel tracking and fuzzy logic techniques was proposed by [PERSON] and [PERSON] [82]. In the first stage, the centerlines of enhanced retinal image were detected, while retinal vessels were filled using Fuzzy C-Means (FCM) clustering technique in the second stage. The final segmented result was obtained by combining centerlines images with fuzzy segmented images. Centerlines are used as initial points for a FCM-based region growing algorithm. The evaluation of the technique resulted in an average accuracy of 72.52% on DRIVE dataset and 77.66% on STARE dataset. A novel scheme based on combination of genetic algorithm and fuzzy c-means clustering algorithm was proposed by [PERSON] and [PERSON] [81]. In pre-processing stage, the green channel of raw retinal image was extracted and enhanced by histogram equalization. Then, the retinal image was divided into two major layers: texture layer and smooth layer. Texture layer was directly fed as input to the processing stage due to the amount of information that contains. The features, containing data obtained in the first stage, were clustered via fuzzy c-means algorithm in conjunction with genetic algorithm, where, firstly, the genetic algorithm is used to obtain the approximate solution of the global optimal solution; and, secondly, the approximate solution was used as initial value of the fuzzy c-means algorithm. Genetic algorithm eases and enhances the duty of fuzzy c-means in finding the optimal solution without falling into issue of local detection of optimal solutions. To overcome the issues related to the objective function of the classic fuzzy c-means classifier, [PERSON] et al. [83] utilized the possibilistic version of fuzzy c-means algorithm optimized by Cuckoo search algorithm. They used the new clustering methods, possibilistic c-means proposed by [125] and possibilistic fuzzy c-means proposed by [126]m to establish an optimal version of fuzzy c-means that were used accordingly for retinal vessels segmentation. The optimality of the proposed method was examined by the heuristic search algorithm Cuckoo Search (CS). The evaluation on the STARE dataset indicated an average accuracy, specificity and sensitivity of 0.94478, 0.987 and 0.586, respectively, whereas, for DRIVE dataset, the measures were 0.938, 0.984 and 0.628, respectively.