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

Yitian Zhao, Yalin Zheng, Yonghuai Liu, Yifan Zhao, Lingling Luo, Siyuan Yang, Tong Na, Yongtian Wang, Jiang Liu

Research output: Journal PublicationArticlepeer-review

75 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8049478
Pages (from-to)438-450
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume37
Issue number2
DOIs
Publication statusPublished - Feb 2018
Externally publishedYes

Keywords

  • Symmetry filter
  • angiography
  • enhancement
  • local phase
  • vascular

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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