## 3 Experimental Results
### Segmentation Results  
This section presents the process to determine the most suitable binary classification technique to be used in the fourth step of the proposed method. Later, the evaluation of the segmentation performance of the proposed method is presented in a comparative analysis approach with other blood-vessel segmentation methods from the state of the art. For the evaluation of the blood vessels classification efficiency, the accuracy metric is employed. To select the most suitable technique for the segmentation step of the proposed method, fourteen binary classification tools are compared using the detection response of the multiscale analysis performed in the previous steps. Thirteen methods of the comparative set are automatic binary classification techniques frequently used in vessel-segmentation literature. The remaining method is a soft computing approach which selects the threshold value from the detection response scale that maximizes the accuracy metric. The soft computing procedure followed to define the most appropriate threshold value is illustrated in Figure 9, where the best value found is 0.87 in the detection response scale using the training set of 100 images and a full search strategy. Figure 7: ROC curves obtained by the eleven vessel detection methods, and the proposed method, using the test set of 30 images. Figure 8: (**a**) Ten original angiograms from the test set of 30 images, and (**b**) their respective delineated ground-truth. The following rows present the detection responses obtained by using (**c**) the proposed method, (**d**) the multiscale Gaussian matched filters, (**e**) the multiscale Gabor filter, (**f**) the CNN ([PERSON] and Zisserman) method, (**g**) the method of [PERSON] et al. [11], (**h**) the CNN ([PERSON] et al.) method, (**i**) the method of [PERSON] and [PERSON] [7], (**j**) the method of [PERSON] et al. [25], (**k**) the method of [PERSON] and [PERSON] [10], (**l**) the method of [PERSON] and [PERSON] [1], (**m**) the method of [PERSON] et al. [13], and (**n**) the method of [PERSON] et al. [3]. In addition, Table 4 presents the comparative analysis of the fourteen automatic binary classification tools in terms of six different binary evaluation metrics, using the test set of 30 angiograms. The analysis is performed by applying each of the compared segmentation tools to the multiscale response of the proposed method, which is illustrated in Figure 10. After visual examination of Figure 10, it can be noticed that the soft computing approach provides the lowest incorrect classification rate of background pixels as vessel features, reflected in its high specificity. On the other hand, most of the other classification tools have presented high sensitivity at the cost of a considerable high number of false positives that results in a low positive predictive value, whereas the fixed threshold performs the correct classification of vessel pixels with a significant rate of positive predictive value. Moreover, the Dice coefficient achieved by the fixed threshold is significantly higher than the obtained by the other binary classification methods, being the soft computing approach the one that provides the most similar vessel segmentation to the ground-truth. Because of its high overall segmentation performance presented in the comparative analysis, the best classification technique to segment the multiscale detection response is the binarization under a threshold value of 0.87 obtained with a soft computing procedure.