Automatic segmentation of coronary arteries using a multiscale Top-Hat operator and multiobjective optimization

Ivan Cruz Aceves,Arturo Hernandez Aguirre,Juan Gabriel Aviña Cervantes

doi:10.21640/ns.v7i15.362

Ivan Cruz Aceves, Arturo Hernandez Aguirre + Show 1 more

Open Access

https://doi.org/10.21640/ns.v7i15.362

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Abstract

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.

Highlights

Automatic segmentation of coronary arteries in X-ray angiography images can help cardiologists for diagnosing and treating vessel abnormalities
In order to evaluate the performance of the segmentation methods, 40 of the 80 angiograms have been used as training set primarily for tuning the parameters of the methods, and the remaining 40 angiograms have been used as an independent test set for evaluation of vessel segmentation methods
Automatic segmentation of coronary arteries using a multiscale Top-Hat operator and multiobjective optimization set of angiograms

Summary

Introduction

Automatic segmentation of coronary arteries in X-ray angiography images can help cardiologists for diagnosing and treating vessel abnormalities. The development of efficient methods for automatic vessel segmentation has become essential for computer-aided diagnosis (CAD) systems. Since the presence of several shades along blood vessels generates multimodal histograms, the vessel segmentation problem has been commonly addressed in two different steps. The first step is vessel enhancement, where the methods try to remove noise from the image and to enhance the vessel-like structures. The second step consists in the classification of vessel and nonvessel pixels by using different strategies such as the selection of an optimal threshold value

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Conclusion