Cervantes-Sanchez Fernando, Cruz-Aceves Ivan, Hernandez-Aguirre Arturo, Aviña-Cervantes Juan Gabriel, Solorio-Meza Sergio, Ornelas-Rodriguez Manuel, Torres-Cisneros Miguel
Centro de Investigación en Matemáticas (CIMAT), A.C., Jalisco S/N, Col. Valenciana, 36000 Guanajuato, GTO, Mexico.
CONACYT, Centro de Investigación en Matemáticas (CIMAT), A.C., Jalisco S/N, Col. Valenciana, 36000 Guanajuato, GTO, Mexico.
Comput Intell Neurosci. 2016;2016:2420962. doi: 10.1155/2016/2420962. Epub 2016 Sep 25.
This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA) in X-ray angiograms. Since the single-scale Gabor filters (SSG) are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area ( ) under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with = 0.9502 over a training set of 40 images and = 0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.
本文提出了一种在X射线血管造影中使用玻尔兹曼单变量边际分布算法（BUMDA）来改进单尺度伽柏滤波器训练步骤的新方法。由于单尺度伽柏滤波器（SSG）由三个参数控制，为了在减少计算时间的同时最大化冠状动脉的检测性能，非常需要对SSG参数进行优化选择。为了获得SSG的最佳参数集，将接收器操作特性曲线下的面积（ ）用作适应度函数。此外，为了从伽柏滤波器响应中对血管和非血管像素进行分类，采用了类间方差阈值化方法。使用所提出方法的实验结果在40幅图像的训练集上获得了最高检测率， = 0.9502，在40幅图像的测试集上 = 0.9583。此外，血管分割的实验结果在血管造影测试集上提供了0.944的准确率。
