## 3 Experimental Results
### Detection Results  
To evaluate the performance of the detection step of the proposed method, the comparative analysis with respect blood vessel enhancement methods from the state of the art is reported in this section. The proposed method is compared with four artificial neural network based methods, and seven state-of-the-art methods for blood vessel enhancement, using the test set of 30 images. The detection performance of each blood vessel enhancement method is evaluated in terms of the area under the ROC curve. The methods based on neural networks were trained using the training set of 100 images, and were selected from several candidates that were previously tested, and according to their relevant high blood vessel detection performance. The multiscale Gaussian matched filter [17], which employs a multilayer perceptron network with 3 neurons in a first hidden layer, and 8 neurons in a second hidden layer. This method obtains a vessel detection performance of \(A_{z}=0.9362\), using a database of 100 angiograms, and the set of parameters \(l=13\), \(T=15\), \(K=12\) and \(\sigma=[1.5,1.6,\ldots,2.5]\) for the multiscale GMF. The multiscale Gabor filter [16], that uses a multilayer perceptron with two hidden layers with 5 neurons each. This coronary artery detection method achieves a performance of \(A_{z}=0.9520\) using a database of 80 angiograms, whereas the parameters for the multiscale Gabor filters are set as \(l=3\), \(\tau=[2,3,\ldots,20]\) and \(\kappa=45\). The following two methods are convolutional neural networks (CNN) that were originally proposed to perform object recognition in images; however, in this work those architectures are adapted to perform the detection of blood vessels in coronary angiograms. The architecture proposed by [PERSON] et al. [23] which is applied to the multiscale responses of the Gaussian matched filters and Gabor filters, using the same parameters of the proposed method for both multiscale vessel enhancement methods. Finally, the architecture proposed by [PERSON] and [PERSON] [24] is employed using \(32\times 32\) pixel sized patches from the original angiogram without additional processing. The following seven blood vessel enhancement methods are the most referenced in literature. The method of [PERSON] et al. [11] employs the single-scale GMF, where the filter parameters are selected with an algorithm inspired by natural evolution. [PERSON] and [PERSON] [7] proposed to use the single-scale GMF extending the number of orientations to \(K=18\), keeping the other parameters in the original values proposed by [PERSON] et al. [6]. [PERSON] et al. [25] addressed the detection of blood vessels by using a line detector and varying its scale in a range of 1 to 15 pixels. The method of [PERSON] and [PERSON] [10] is based on the single-scale GMF, where the parameters \(T\) and \(L\) that govern the filter are selected by a genetic algorithm from the evolutionary computation family, preserving the orientations in \(K=12\). The mathematical morphology based method of [PERSON] and [PERSON] [1] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rank**} & \multirow{2}{*}{**Configuration**} & **Number of** & **Architectures** & **Dominated** \\  & & **Dominated** & **Similarly** & **by Other** \\  & & **Architectures** & **Distributed** & **Architectures** \\ \hline
1 & [9, 9] & **90** & 9 & 0 \\
2 & \([10,6]\) & 78 & 21 & 0 \\
3 & \([10,10]\) & 78 & 21 & 0 \\
4 & \([9,5]\) & 77 & 22 & 0 \\
5 & \([10,9]\) & 75 & 24 & 0 \\
6 & \([9,10]\) & 74 & 25 & 0 \\
7 & \([8,8]\) & 72 & 27 & 0 \\
8 & \([9,8]\) & 72 & 27 & 0 \\
9 & \([10,8]\) & 70 & 29 & 0 \\
10 & \([8,10]\) & 69 & 29 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ranking of the best 10 configurations from the 100 comparative architectures of the MLP, according to the number of stochastically dominated architectures. The configurations are presented as \([i,j]\), where \(i\) is the number of neurons in the first hidden layer, and \(j\) the number of neurons in the second hidden layer.