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.
This paper presents a novel method for the automatic design of binary descriptors for the detection of coronary arteries in X-ray angiograms. The method is divided into two diﬀerent steps for detection and segmentation. In the step of automatic vessel detection, the metaheuristic of iterated local search (ILS) is used for the design of optimal binary descriptors for detecting vessel-like structures by using the top-hat transform in the spatial image domain. The detection results are compared with those obtained by five state-of-the-art vessel enhancement methods. The proposed method obtained the highest detection results in terms of the area (Az ) under the ROC curve (Az = 0.9635) using a training set of 50 angiograms, and Az = 0.9544 with an independent test set of 50 X-ray images. In the segmentation step, the inter-class variance thresholding method was applied to classify vessel and nonvessel pixels from the top-hat filter response obtained from the binary descriptor. According to the experimental results, the vessel detection by using an automatically generated binary descriptor can be highly suitable for computer-aided diagnosis.