Abstract
In medical imaging, vesselness diffusion is usually performed to enhance the vessel structures of interest and reduce background noises, before vessel segmentation and analysis. Numerous learning-based techniques have recently become very popular for coronary artery filtering due to their impressive results. In this work, a supervised machine learning method for coronary artery vesselness diffusion with high accuracy and minimal user interaction is designed. The fully discriminative filter learning method jointly learning a classifier the weak learners rely on and the features of the classifier is developed. Experimental results demonstrate that this scheme achieves good isotropic filtering performances on both synthetic and real patient Coronary Computed Tomography Angiography (CCTA) datasets. Furthermore, region growing-based segmentation approach is performed over filtered images obtained by using different schemes. The proposed diffusion scheme is able to achieve higher average performance measures (87.8% ± 1.5% for Dice, 86.5% ± 1.3% for Precision and 88.5% ± 2.6% for Sensitivity). In conclusion, the developed diffusion method is capable of filtering coronary artery structures and suppressing nonvessel tissues, and can be further used in clinical practice as a real-time CCTA images preprocessing tool.
Highlights
Accurate coronary arteries determination is commonly a fundamental step for computer-aided diagnosis of cardiovascular diseases, especially for coronary stenosis quantification [1]
Different diffusion schemes are tested on the Coronary Computed Tomography Angiography (CCTA) images from real patients, to evaluate the segmentation accuracy of the proposed diffusion scheme
We propose an accurate and efficient learningbased vesselness filtering scheme, for the purpose of enhancing coronary arteries and reducing background noises in CCTA images
Summary
Accurate coronary arteries determination is commonly a fundamental step for computer-aided diagnosis of cardiovascular diseases, especially for coronary stenosis quantification [1]. Medical imaging techniques-based vessel detection usually contains two significant tasks, namely vascular structures enhancement in original medical images and vessel segmentation. 3D coronary arteries can be directly segmented based on grayvalue [1], prior knowledge [2,3], deformable model [4] and learning-based method [5]. In Kerkeni et al [6], the author compares four Hessian-based multiscale filters and shows that the vessel enhancement diffusion (VED) filter is superior to the other two methods in enhancing vascular structure and suppressing background noise. Strong diffusion in the vessel direction helps to overcome significant intensity decline. Despite such a large number of vessel
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