A new binary descriptor for the automatic detection of coronary arteries in x-ray angiograms

Ivan Cruz-Aceves; Fernando Cervantes-Sanchez; Martha A. Hernández-González

doi:10.1117/12.2511569

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21 December 2018 A new binary descriptor for the automatic detection of coronary arteries in x-ray angiograms

Ivan Cruz-Aceves, Fernando Cervantes-Sanchez, Martha A. Hernández-González

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Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 1097517 (2018) https://doi.org/10.1117/12.2511569
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico

Abstract

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 (A_z ) under the ROC curve (A_z = 0.9635) using a training set of 50 angiograms, and A_z = 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.

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Ivan Cruz-Aceves, Fernando Cervantes-Sanchez, and Martha A. Hernández-González "A new binary descriptor for the automatic detection of coronary arteries in x-ray angiograms", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 1097517 (21 December 2018); https://doi.org/10.1117/12.2511569

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