Coronary Artery Segmentation and Width Estimation Using Gabor Filters and Evolutionary Computation Techniques

This paper presents a novel method based on single-scale Gabor filters (SSG) consisting of three steps for vessel segmentation and vessel width estimation of X-ray coronary angiograms. In the first stage, a comparative analysis of genetic algorithms, and two estimation of distribution algorithms in order to improve the vessel detection rate of the SSG, while reducing the computational time of the training step is performed. The detection results of the SSG are compared with those obtained by four state-of-the-art detection methods via the area (\(A_z\)) under the receiver operating characteristic (ROC) curve. In the second stage, a comparative analysis of five automatic thresholding methods is performed in order to discriminate vessel and nonvessel pixels from the Gabor filter response. In the last step, a procedure to estimate the vessel width of the segmented coronary tree structure is presented. The experimental results using the SSG obtained the highest vessel detection performance with \(A_z = 0.9584\) with a training set of 40 angiograms. In addition, the segmentation results using the interclass variance thresholding method provided a segmentation accuracy of 0.941 with a test set of 40 angiograms. The performance of the proposed method consisting of the steps of vessel detection, segmentation, and vessel width estimation shows promising results according to the evaluation measures, which is suitable for clinical decision support in cardiology.