## 3 Experimental Results
### Segmentation Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Segmentation Method**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Positive**} \\  & & **Class** & **Specificity** & **Predictive** & **Dice** \\  & & **Accuracy** & & & **Value** & \\ \hline
**Threshold (0.87)** & **0.9698** & 0.8122 & 0.6364 & **0.9880** & **0.7434** & **0.6857** \\ Otsu [26] & 0.9173 & 0.9315 & 0.9472 & 0.9157 & 0.3806 & 0.5430 \\ Ridler and Calvard [27] & 0.9164 & 0.9312 & 0.9478 & 0.9147 & 0.3779 & 0.5403 \\ Niblack [28] & 0.9146 & 0.9252 & 0.9370 & 0.9134 & 0.3717 & 0.5322 \\ Moment-preserving [29] & 0.9081 & **0.9321** & 0.9589 & 0.9053 & 0.3565 & 0.5197 \\ RATS [30] & 0.8989 & 0.9300 & 0.9646 & 0.8953 & 0.3350 & 0.4973 \\ Vessel repair [5] & 0.8902 & 0.9262 & 0.9664 & 0.8860 & 0.3168 & 0.4772 \\ Sauvola [31] & 0.8884 & 0.9241 & 0.9639 & 0.8843 & 0.3130 & 0.4726 \\ Entropy maximization [32] & 0.8853 & 0.9274 & 0.9744 & 0.8804 & 0.3082 & 0.4683 \\ Background unification [1] & 0.8757 & 0.9213 & 0.9721 & 0.8704 & 0.2909 & 0.4478 \\ Degree-based [33] & 0.8581 & 0.9176 & 0.9840 & 0.8512 & 0.2656 & 0.4183 \\ White [34] & 0.8574 & 0.9098 & 0.9682 & 0.8514 & 0.2627 & 0.4132 \\ Histogram concavity [35] & 0.8442 & 0.9117 & 0.9871 & 0.8363 & 0.2480 & 0.3965 \\ Local entropy [36] & 0.8247 & 0.9028 & **0.9900** & 0.8156 & 0.2270 & 0.3693 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation accuracy obtained by the comparative threshold methods applied to the multiscale detection response by using the testing set of 30 angiograms. Figure 9: Soft computing procedure for the selection of the threshold value that maximizes the blood vessels segmentation accuracy using the training set of angiograms. Figure 10: (**a**) Ten angiograms from the testing set of 30 images, and (**b**) their respective ground-truth. The following rows are the segmentation results of (**c**) the fixed threshold (0.87) method, (**d**) the method of [PERSON] [26], (**e**) the method of [PERSON] and [PERSON] [27], (**f**) the method of [PERSON] [28], (**g**) the Moment-preserving [29] method, (**h**) the RATS [30] method, (**i**) the Vessel repair [5] method, (**j**) the method of [PERSON] [31], (**k**) the Entropy maximization [32] method, (**l**) the Background unification [1] method, (**m**) the Degree-based [33] method, (**n**) the method of [PERSON] [34], (**o**) the Histogram concavity [35] method, and (**p**) the Local entropy [36] method. Moreover, in order to evaluate the results of the proposed method, twelve specialized vessel segmentation methods of the state of the art have been selected to perform a comparative analysis. From the set of comparative methods, the method introduced by [PERSON] and [PERSON] [1] uses the Top-hat operator for coronary arteries enhancement in angiograms, which combined with a background unification algorithm, extracts vessel features from coronary angiograms. [PERSON] et al. [2] used the background unification algorithm on the multiscale response of the Top-hat operator, applied at three different scales over the original angiogram. [PERSON] and [PERSON] [37] addressed the vessel segmentation from the single-scale GMF response. The binary segmentation is obtained by selecting the threshold value that maximizes the local entropy computed from the co-occurrence matrix. [PERSON] et al. [8] applies two different enhancement methods to the original image: the Top-hat operator, and the single-scale GMF. The segmentation result is obtained with a threshold level that maximizes the entropy of the gray-scale intensity of each detection response. The method proposed by [PERSON] et al. [38] introduces a segmentation procedure based on a similarity degree between pixels of the gray-scale detection response. The approach of [PERSON] et al. [25] segments vessel pixels from the multiline detector response by using a fixed threshold value of 0.56. The value is selected as the binarization level that maximizes the classification accuracy using a training set of images.