Automatic 2D/3D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue as a result of continuing difficulties posed by such factors as poor contrast, inhomogeneous backgrounds, and presence of noise during image acquisition. In this paper, we propose a novel 2D/3D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only takes into account local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance in the position of the respective contours. As a result this filter shows a strong response to the vascular features under typical imaging conditions. Results based on publicly accessible datasets demonstrate its superior performance to other state-of-the-art methods.