A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms

摘要

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 (A(z)) under the receiver operating characteristic (ROC) curve. In the second stage, the interclass 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 A(z) value and the A(z) 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. (C) 2016 Elsevier Ltd. All rights reserved.