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A novel method for the design of convolutional gray-level templates for the automatic detection of coronary arteries
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Paper Abstract

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.

Paper Details

Date Published: 3 January 2020
PDF: 9 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113301B (3 January 2020); doi: 10.1117/12.2542561
Show Author Affiliations
Ivan Cruz-Aceves, Ctr. de Investigación en Matemáticas, A.C. (Mexico)
Fernando Cervantes-Sanchez, Ctr. de Investigación en Matemáticas, A.C. (Mexico)
Arturo Hernández-Aguirre, Ctr. de Investigación en Matemáticas, A.C. (Mexico)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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