## 1 Introduction
Later, [PERSON] and [PERSON] [10] proposed a method from the Evolutionary Computation algorithms (EC) to reduce the computational time consumed by the exhaustive search. More recently, [PERSON] et al. [11] performed a study to determine the most suitable EC method for the automatic selection of parameters for the GMF applied to the coronary artery segmentation problem. An alternative to the analysis of the image in the spatial domain is the use of Gabor filters in the frequency domain. Gabor filters for blood vessel enhancement in medical images was proposed by [PERSON] et al. [12] based on its capability of detecting oriented features. Similarly to GMF, Gabor filters can be rotated into different directions in order to detect vessel-like structures in multiple orientations. An important modification to the Gabor filter was proposed by [PERSON] et al. [13], where a simplified definition of the filter is presented, in addition to the use of only the real part of the filter, discarding the imaginary part which is commonly used to detect edge features. For Gabor filters, recent methods have been proposed to select the most appropriate parameters for the detection of blood vessels in image data. The method proposed by [PERSON] et al. [13; 14] performs a full-search for the parameters of elongation, average width, and number of oriented filters before selecting the most suitable values for the detection of blood vessels in retinal fundus images. Later, [PERSON] et al. [15] proposed the use of a Boltzmann based EC algorithm for the automatic selection of the Gabor filter parameters for the coronary artery segmentation problem. Considering that the filter responses obtained by the blood vessel detection methods can be treated as multilevel classifiers, the Receiver Operating Characteristic curve analysis (ROC) has been frequently used to evaluate and compare their vessel detection efficiency. Gabor filters have shown the best detection performance in coronary arteries compared to the state-of-the-art approaches [15,16], followed in efficiency by the Gaussian matched filters [11]. Because these two methods have obtained superior performance to carry out the automatic detection of blood vessels in medical images, in this work they are thoroughly studied. However, it is important to point out that coronary arteries present a natural variant thickness along their span; consequently, a multiscale approach is convenient in order to enhance vessel-like structures properly. In this paper a novel method that uses the multiscale filter responses of the GMF and Gabor filters to improve the detection of coronary arteries in X-ray angiograms is presented. The proposed method addresses the limitation that single-scale versions of the GMF and Gabor filters present when enhancing vessel-like structures of a unique thickness at a time. In the method, a multilayer perceptron network (MLP) is fed with the multiscale filter responses of the GMF and Gabor filters methods in order to obtain an improved detection response using information from the spatial and frequency domains of the X-ray image. In addition, to select the optimal MLP configuration for detecting coronary arteries, a hypothesis test for stochastic domination between multiple possible architectures has been performed. To evaluate the detection step of the proposed method, eleven methods for blood vessel enhancement are compared with the proposed method in terms of the area under the ROC curve. Finally, in the segmentation step of the proposed method, twelve specialized blood vessel segmentation methods are used for comparative analysis in terms of six binary classification metrics. The organization of this paper is the following. In Section 2, the basis of the Gaussian matched filters, Gabor filters and Artificial Neural Networks, along with the proposed method including multiscale analysis, are described in detail. Computational experiments including numerical and qualitative results are discussed in Section 3, and finally, the concluding remarks can be found in Section 4.