Automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization. (March 2016)

• Record Type: Journal Article
• Authors: Cruz-Aceves, Ivan; Oloumi, Faraz; Rangayyan, Rangaraj M.; Aviña-Cervantes, Juan G.; Hernandez-Aguirre, Arturo
• Is Part Of: Biomedical signal processing and control. Volume 25(2016)
• Page Start: 76
• Page End: 85
• Publication Date: 2016-03
• Subjects: Automatic segmentation -- Coronary angiograms -- Gabor filters -- Medical imaging -- Multiobjective optimization -- Vessel detection; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.11.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: A new method for automatic segmentation of coronary arteries is proposed. The high detection rate achieved is represented by an area under ROC curve of A z = 0.961 with a training set of 40 images. The multiscale Gabor filters demonstrated high detection accuracy with A z = 0.952 with a test set of 40 angiograms. The multiobjective thresholding method provided the best average accuracy (0.881). Abstract: This paper presents a new method consisting of two stages for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the first stage, multiscale Gabor filters are used to detect vessel structures in the angiograms. The results of multiscale Gabor filtering are compared with those obtained by applying multiscale methods based on the top-hat operator, Hessian matrix, and Gaussian matched filters. The performance of the vessel-detection methods is evaluated through the area ( A z ) under the receiver operating characteristic (ROC) curve. In the second stage, coronary arteries are segmented by binarizing the magnitude response of Gabor filters using a new thresholding method based on multiobjective optimization, which is compared with seven thresholding methods. Measures of sensitivity, specificity, accuracy, and positive predictive value are used to analyze the segmentation methods, by comparing the results to the ground-truth markings of the vessels drawn by a specialist. Finally, the proposed method is compared with seven state-of-the-art vessel segmentation methods. The result of vessel detection using multiscale Gabor filters demonstrated high accuracy with A z = 0.961 with a training set of 40 angiograms and A z = 0.952 with an independent test set of 40 angiograms. The results of vessel segmentation with the multiobjective thresholding method provided an average accuracy of 0.881 with the test set of angiograms.