Fast and robust 3D numerical method for coronary artery vesselness diffusion from CTA images

Abstract

Optimized anisotropic diffusion is commonly used in medical imaging for the purpose of reducing background noise and tissues and enhancing the vessel structures of interest. In this work, a hybrid diffusion tensor is developed, which integrates Frangi’s vesselness measure with a continuous switch, suitable for filtering both tubular and planar image structures. Besides, a new 3D diffusion discretization scheme is proposed, in which we apply Gaussian kernel decomposition for computing image derivatives. This scheme is rotational invariant and shows good isotropic filtering properties on both synthetic and real Computed Tomography Angiography (CTA) data. In addition, segmentation approach is performed over filtered images obtained by using different schemes. Our method is proved to give better segmentation result and more thin branches can be detected. In conclusion, the proposed method should garner wider clinical applicability in Computed Tomography Coronary Angiography (CTCA) images preprocessing.