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Open AccessArticle

Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks

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Centro de Investigación en Matemáticas, A.C. (CIMAT), Guanajuato 36023, Gto, Mexico
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CONACYT-Centro de Investigación en Matemáticas, A.C. (CIMAT), Guanajuato 36023, Gto., Mexico
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Unidad Médica de Alta Especialidad (UMAE)-Hospital de Especialidades No.1. Centro Médico Nacional del Bajio, IMSS, CP. León 37320, Guanajuato, Mexico
4
Department of Health Sciences, Universidad Tecnológica de México (UNITEC) Campus León, León 37200, Gto., Mexico
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5507; https://doi.org/10.3390/app9245507
Received: 22 October 2019 / Revised: 10 December 2019 / Accepted: 10 December 2019 / Published: 14 December 2019
(This article belongs to the Special Issue Signal Processing and Machine Learning for Biomedical Data)
This paper presents a novel method for the automatic segmentation of coronary arteries in X-ray angiograms, based on multiscale analysis and neural networks. The multiscale analysis is performed by using Gaussian filters in the spatial domain and Gabor filters in the frequency domain, which are used as inputs by a multilayer perceptron (MLP) for the enhancement of vessel-like structures. The optimal design of the MLP is selected following a statistical comparative analysis, using a training set of 100 angiograms, and the area under the ROC curve ( A z ) for assessment of the detection performance. The detection results of the proposed method are compared with eleven state-of-the-art blood vessel enhancement methods, obtaining the highest performance of A z = 0.9775 , with a test set of 30 angiograms. The database of 130 X-ray coronary angiograms has been outlined by a specialist and approved by a medical ethics committee. On the other hand, the vessel extraction technique was selected from fourteen binary classification algorithms applied to the multiscale filter response. Finally, the proposed segmentation method is compared with twelve state-of-the-art vessel segmentation methods in terms of six binary evaluation metrics, where the proposed method provided the most accurate coronary arteries segmentation with a classification rate of 0.9698 and Dice coefficient of 0.6857 , using the test set of angiograms. In addition to the experimental results, the performance in the detection and segmentation steps of the proposed method have also shown that it can be highly suitable for systems that perform computer-aided diagnosis in X-ray imaging. View Full-Text
Keywords: automatic segmentation; coronary arteries; medical imaging; multiscale Gabor filters; Multiscale Gaussian Filters; multilayer perceptron automatic segmentation; coronary arteries; medical imaging; multiscale Gabor filters; Multiscale Gaussian Filters; multilayer perceptron
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MDPI and ACS Style

Cervantes-Sanchez, F.; Cruz-Aceves, I.; Hernandez-Aguirre, A.; Hernandez-Gonzalez, M.A.; Solorio-Meza, S.E. Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks. Appl. Sci. 2019, 9, 5507.

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