Vascular structure segmentation and bifurcation detection

Jinghao Zhou, Sukmoon Chang, Dimitris Metaxas, Leon Axel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

Delineation and reconstruction of vascular structures in medical images are critical for the diagnosis of various vascular diseases and related surgical procedures. In this paper, we present a novel method for vascular structure segmentation with fully automatic detection of bifurcation points. First, we perform a preselection of tubular objects and trace the vessels based on me eigenanalysis of the Hessian matrix. This provides us the estimated direction of vessels as well as the cross-sectional planes orthogonal to the vessels. Then, we apply AdaBoost learning method with specially designed filters on crosssectional planes to automatically detect the bifurcation points of the vessels. Our method has over 97% success rate for detecting bifurcation points. We present very promising results of our method applied to the reconstruction of pulmonary vessels from clinical chest CT. Our method allows for fully automatic detection of bifurcation points as well as segmentation of vessels.

Original languageEnglish (US)
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages872-875
Number of pages4
DOIs
StatePublished - Nov 27 2007
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: Apr 12 2007Apr 15 2007

Publication series

Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

Other

Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Country/TerritoryUnited States
CityArlington, VA
Period4/12/074/15/07

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Medicine(all)

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