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

• Published in: IEEE transactions on medical imaging Vol. 37; no. 2; pp. 438 - 450
• Main Authors: Zhao, Yitian, Zheng, Yalin, Liu, Yonghuai, Zhao, Yifan, Luo, Lingling, Yang, Siyuan, Na, Tong, Wang, Yongtian, Liu, Jiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; angiography; Angiography - methods; Automation; Blood vessels; Databases, Factual; enhancement; Humans; Image acquisition; Image contrast; Image detection; Image edge detection; Image enhancement; Image segmentation; Imaging; Imaging, Three-Dimensional - methods; local phase; Multimodal Imaging - methods; Multiresolution analysis; Nonhomogeneous media; Retinal Vessels - diagnostic imaging; Symmetry; Symmetry filter; Three-dimensional displays; Two dimensional displays; vascular
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2017.2756073

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 because of difficulties posed by multiple factors, such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers 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 of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.