This paper presents a novel method for the automatic design of convolutional gray-level templates for detecting coronary arteries in X-ray angiograms. The proposed method uses the metaheuristic of iterated local search (ILS) to address the high-dimensional problem (O(256n)) involved in the design of convolutional templates. This automatically generated template is convolved in the spatial domain at different orientations to form a directional filter bank in order to detect coronary arteries at different angular resolutions. The vessel detection results are compared with those obtained by four state-of-the-art vessel enhancement methods in terms of the area (Az) under the receiver operating characteristic (ROC) curve. The proposed method achieved the highest detection results with Az = 0.9405 using a training set of 50 angiograms. Moreover, the convolutional gray-level template obtained from the training step, it was directly evaluated with an independent test set of 50 X-ray angiograms obtaining an Az = 0.9565, which is the highest performance according to the comparative analysis. In addition to the experimental results, the use of metaheuristics for designing convolutional gray-level templates obtains suitable results to be considered in systems that perform computer-aided diagnosis, and it also represents an encouraging area for future research.
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