Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks
Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks
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DOI：
10.3390/app9245507
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发表时间：
2019-12-01
影响因子：
2.7
通讯作者：
Eduardo Solorio-Meza, Sergio
中科院分区：
文献类型：
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作者：
Cervantes-Sanchez, Fernando;Cruz-Aceves, Ivan;Eduardo Solorio-Meza, Sergio
提出了一种基于多尺度分析和神经网络的冠状动脉自动分割方法。多尺度分析是通过使用高斯滤波器在空间域和Gabor滤波器在频域，这是作为输入的多层感知器（MLP）的增强血管状结构。MLP的最佳设计是在统计比较分析之后选择的，使用100张血管造影片的训练集和用于评估检测性能的ROC曲线下面积（Az）。所提出的方法的检测结果进行了比较，与11个国家的最先进的血管增强方法，获得最高性能的Az=0.9775，与30幅血管造影片的测试集。一位专家概述了包含130张X射线冠状动脉血管造影照片的数据库，并获得了医学伦理委员会的批准。另一方面，血管提取技术是从应用于多尺度滤波器响应的14个二进制分类算法中选择的。最后，所提出的分割方法进行了比较，与十二个国家的最先进的血管分割方法的六个二进制评价指标，其中所提出的方法提供了最准确的冠状动脉分割的分类率为0.9698和骰子系数为0.6857，使用测试集的血管造影片。除了实验结果之外，所提出的方法在检测和分割步骤中的性能也表明，它可以高度适用于在X射线成像中执行计算机辅助诊断的系统。
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 (Az) 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 Az=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.