Data-Dependent Higher-Order Clique Selection for Artery–Vein Segmentation by Energy Minimization

Yoshiro Kitamura, Yuanzhong Li, Wataru Ito, Hiroshi Ishikawa

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

We propose a novel segmentation method based on energy minimization of higher-order potentials. We introduce higher-order terms into the energy to incorporate prior knowledge on the shape of the segments. The terms encourage certain sets of pixels to be entirely in one segment or the other. The sets can for instance be smooth curves in order to help delineate pulmonary vessels, which are known to run in almost straight lines. The higher-order terms can be converted to submodular first-order terms by adding auxiliary variables, which can then be globally minimized using graph cuts. We also determine the weight of these terms, or the degree of the aforementioned encouragement, in a principled way by learning from training data with the ground truth. We demonstrate the effectiveness of the method in a real-world application in fully-automatic pulmonary artery–vein segmentation in CT images.

Original languageEnglish
Pages (from-to)142-158
Number of pages17
JournalInternational Journal of Computer Vision
Volume117
Issue number2
DOIs
Publication statusPublished - 2016 Apr 1

Keywords

  • Artery–vein segmentation
  • Higher-order energy
  • Segmentation
  • Surgery simulation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Data-Dependent Higher-Order Clique Selection for Artery–Vein Segmentation by Energy Minimization'. Together they form a unique fingerprint.

Cite this