SUMMARY
This paper presents a new coronary artery segmentation method in X-ray angiographic images consisting of two stages. In the first stage, a multiscale top-hat operator based on the properties of the Hessian matrix is introduced to enhance vessel-like structures in the angiogram. The results of the proposed multiscale top-hat operator are compared with multiscale methods based on Gaussian matched filters, Hessian matrix and morphological operators, and analyzed using the area ( Az ) under the receiver operating characteristic curve. In the second stage, a new thresholding method based on multiobjective optimization following the weighted sum approach to classify vessel and nonvessel pixels is presented. The performance of the multiobjective method is compared with seven automatic thresholding methods using the ground-truth angiograms drawn by a specialist with the sensitivity, specificity and accuracy measures. Finally, the proposed method is compared with five state-of-the-art vessel segmentation methods. The vessel enhancement results using the multiscale top-hat operator demonstrated the highest accuracy with Az = 0.942 with a training set of 40 angiograms and Az = 0.965 with a test set of 40 angiograms. The results of coronary artery segmentation using the multiobjective thresholding method provided an average accuracy performance of 0.923 with the test set of angiograms.