A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms. (July 2016)

• Record Type: Journal Article
• Authors: Cruz-Aceves, Ivan; Cervantes-Sanchez, Fernando; Hernandez-Aguirre, Arturo; Perez-Rodriguez, Ricardo; Ochoa-Zezzatti, Alberto
• Is Part Of: Computers & electrical engineering. Volume 53(2016)
• Page Start: 263
• Page End: 275
• Publication Date: 2016-07
• Subjects: Automatic segmentation -- Coronary arteries -- Entropy -- Gaussian matched filters -- Univariate marginal distribution algorithm -- Vessel detection; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.05.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A new method for automatic segmentation of coronary arteries in X-ray angiograms is proposed. The proposed entropy minimization function obtains 0:97 of similarity with respect to the optimal A z value with the whole set of angiograms. The proposed Gaussian matched filter demonstrated high detection performance with A z = 0:945 with a test set of 45 angiograms. The proposed vessel segmentation method provided the highest accuracy (0:961) with the test set of angiograms. Graphical abstract: Abstract: This paper presents a new method for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the vessel detection stage, a novel Gaussian matched filter (GMF) based on an entropy minimization fitness function is used to detect blood vessels in angiographic images. The detection results of the proposed Gaussian matched filter are compared with those obtained by five state-of-the-art GMF-based methods using the area ( Az ) under the receiver operating characteristic (ROC) curve. In the second stage, the inter-class variance thresholding method has proven to be the most efficient compared with six different methods in order to classify vessel and non vessel pixels from the Gaussian filter response using the accuracy measure and the ground-truth angiograms drawn by a specialist. Finally, the proposed method is compared with eight state-of-the-art vessel segmentation methods. Due to the high rating of similarity (0.97) between the highest Az value and the Az value acquired by the fitness function over the whole dataset of angiograms, the result of vessel detection using the proposed GMF demonstrated high performance achieving A z = 0.945 with a test set of 45 angiograms. In addition, the results of vessel segmentation with the inter-class variance thresholding method provided an accuracy of 0.961 with the test set of angiograms.