## 3 Experimental Results
### Segmentation Results
By visual inspection of Figure 11, it can be noticed that the method of GMF/Local Entropy [37] presents responses with a high rate of false positive pixels, obtaining the lowest segmentation accuracy of the comparison, this problem is also reflected in the low positive predictive value. The method of GMF/Degree-based [38] can obtain a relative high number of false negative pixels, affecting the segmentation accuracy and sensitivity of the method. The method based on single-scale Gabor filters [15] presents a frequent problem on the resulting image edges reducing the sensitivity of the method. The high false positive rate in those areas is associated to the strong detection intensities obtained by using only the average vessel width as single scale. In the method based on multiscale Gaussian templates [17], the accuracy and positive predictive value are affected by the incorrect classification of the pixels that are closer to the main coronary artery. This problem is because of the strong detection responses obtained by the multiscale Gaussian matched filter for small tubular structures in the background. The methods Hessian Matrix-based/Vessel Repair [5], and Multiline Detector/Threshold (0.56) [25] achieve similar classification accuracy. However, their segmentation performance is affected by the presence of irregular edges, holes, and broken vessels, being the method of Hessian Matrix-based/Vessel Repair [5] the most affected between both, reducing its segmentation sensitivity. The methods Top-hat/Background Unification [1], and Multiscale Top-hat/Background Unification [2] achieve high classification accuracy, only limited by the frequent presence of irregular borders and broken vessels in the segmentation result which reduces their positive predictive value and Dice similarity coefficient with the ground-truth. The U-Net ([PERSON] et al.) [39] method presents the highest specificity and positive predictive value; however, the segmentation result show a high rate of false negative pixels in several images, resulting in broken vessels and a reduction of its overall vessel extraction performance. The method of GMF/Entropy Maximization [8] presents high segmentation results in overall, being affected only by the presence of holes, broken vessels, and false positives in image edges, reducing its positive predictive value. On the other hand, the experimental results obtained by the proposed method achieved the highest segmentation accuracy, specificity, positive predictive value, and Dice similarity coefficient, using the testing set of the present database. Because of the suitable performance of the proposed method, the segmented coronary arteries present a low rate of false positives along with a low presence of holes and isolated vessels. The comparison of the average execution time of the proposed method, and twelve vessel segmentation methods from the state of the art, is presented in Table 6. The methods of GMF/Entropy Maximization [8] and GMF/Degree-based [38] present the lowest computational times, followed by the methods Top-hat/Background Unification [1] and GMF/Local Entropy [37], which low computational time can be attributed to the single-scale analysis performed in the detection step. The methods of Multiscale Top-hat/Background Unification [2] and Multiline Detector/Threshold (0.56) [25] employ a multiscale analysis during the detection step, which increases the execution time with respect to single-scale approaches. The Single-scale Gabor filters/Otsu [15] involve transformations between the space and frequency domain that affects the overall execution time. The Multiscale GMF/Otsu [17] method performs a multiscale analysis in the spatial domain, which demand a greater execution time than other single-scale GMF methods. The Hessian Matrix-based/Vessel Repair [5] method employs a multiscale approach during the detection step, and a vessel repair process in the segmentation step. This method requires a greater execution time than other multiscale methods and is reflected on its average execution time.