## 3 Experimental Results
### Segmentation Results
The method of [PERSON] et al. [5] defines a vessel probability map from a coronary angiogram using a Hessian matrix based method. The vessel segmentation is performed by a region growing algorithm and a subsequent vessel detail repairing procedure. The inter-class variance maximization method of [PERSON] [26] has been an efficient separation tool employed by the following methods. The method of [PERSON] et al. [15] employs the single-scale Gabor filter, which parameters are selected using a nature inspired algorithm for numerical optimization. The U-Net [39] is a convolutional neural network that has been widely used for biomedical image segmentation tasks in recent years. For the comparative analysis, the U-Net [39] was trained using the training set of 100 images, by taking patches of size \(32\times 32\) pixels from the zero-padded angiograms as inputs, and using the cross entropy function as loss metric. Finally, the method proposed by [PERSON] et al. [17] introduced the multiscale GMF that improves the detection performance of its single-scale version. The result of the comparative analysis of the twelve specialized vessel segmentation methods, and the proposed method, is presented in Table 5. The test set of 30 angiograms is used to evaluate the segmentation methods in terms of binary classification accuracy. According to Table 5, the proposed method obtains the highest segmentation performance with respect to the twelve methods from the state of the art by reaching a classification accuracy of 0.9698. The resulting segmentation responses of the comparative segmentation methods are illustrated in Figure 11. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Segmentation Method**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**TN**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**FN**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Positive**} \\  & & & & & & **Class** & **Sensitivity** & **Specificity** & **Predictive** & **Dice** \\ \hline
**Proposed method/Threshold (0.87)** & 89089 & 2529250 & 30750 & 56911 & **0.9698** & 0.8122 & 0.6364 & 0.9880 & 0.7434 & **0.6857** \\ GMF/ Entropy Maximization [8] & 95244 & 2515257 & 44743 & 44756 & 0.9669 & 0.8314 & 0.6803 & 0.9825 & 0.6804 & 0.6803 \\ U-Net (Ronneberger et al.) [39] & 59019 & 2545005 & 14995 & 80981 & 0.9645 & 0.7079 & 0.4216 & **0.9941** & **0.7974** & 0.5515 \\ Multiscale Top-hat/Background Unification [2] & 92073 & 2500198 & 59802 & 49297 & 0.9601 & 0.8172 & 0.6577 & 0.9766 & 0.6062 & 0.6309 \\ Top-hat/Background Unification [1] & 93724 & 2497734 & 62266 & 46276 & 0.9598 & 0.8226 & 0.6695 & 0.9757 & 0.6008 & 0.6333 \\ Multiline Detector/Threshold (0.56) [25] & 121286 & 2459149 & 100851 & 18714 & 0.9557 & 0.9135 & 0.8663 & 0.9606 & 0.5460 & 0.6698 \\ Hessian Matrix-based Resel Repair [5] & 90769 & 2486287 & 37713 & 49231 & 0.9545 & 0.8098 & 0.6484 & 0.9712 & 0.5518 & 0.5962 \\ Multiscale GMF/Otsu [17] & 130560 & 26267934 & 192066 & 9440 & 0.9326 & **0.9288** & 0.9326 & 0.9250 & 0.4047 & 0.5644 \\ CNN ([PERSON] and Zisserman) [24] & 123717 & 22888894 & 171151 & 16283 & 0.9306 & 0.9084 & 0.8837 & 0.9331 & 0.4196 & 0.5690 \\ Single-scale Gabor filters/Otsu [15] & 54876 & 2473308 & 86692 & 85124 & 0.9196 & 0.6791 & 0.3920 & 0.9661 & 0.3876 & 0.3898 \\ CNN ([PERSON] et al.) [23] & 125764 & 232732 & 2325268 & 14246 & 0.9076 & 0.9032 & 0.8983 & 0.9081 & 0.3483 & 0.5020 \\ GMF/Degree-based [38] & 92169 & 2330746 & 229254 & 47831 & 0.8974 & 0.7844 & 0.6584 & 0.9104 & 0.2868 & 0.3995 \\ GMF/Local Entropy [37] & 132769 & 2214091 & 345909 & 7231 & 0.8692 & 0.9066 & **0.9484** & 0.8649 & 0.2774 & 0.4292 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the segmentation performance of twelve vessel segmentation methods from the state of the art, and the proposed method, in terms of six binary segmentation metrics. The testing set of 30 images is used to evaluate the classification performance.