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Vascular Atlas Formation Using a Vessel-to-Image Affine Registration Method

  • Dini Chillet
  • Julien Jomier
  • Derek Cool
  • Stephen Aylward
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2878)

Abstract

We have developed a method for forming vascular atlases using vascular distance maps and a novel vascular model-to-image registration method. Our atlas formation process begins with MR or CT angiogram data from a set of subjects. We extract blood vessels from those data using our tubular object segmentation method. One subject’s vascular network model is then chosen as a template, and its vascular distance map (DM) image is computed. Each of the remaining vascular network models is then registered with the DM template using our vascular model-to-image affine registration method. The DM images from the registered vascular models are then computed. The mean and variance images formed from those registered DM images are the vascular atlas. In this paper we apply the atlas formation process to build atlases of normal brain and liver vasculature. We use Monte Carlo simulations to demonstrate the reliability of the underlying registration method. Additionally, we explain the clinical potential of those atlases and conduct z-score analyses to compare individuals with the atlases to detect abnormal vessels.

Keywords

Vascular Network Registration Method Strong Genetic Component Vascular Model Affine Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Dini Chillet
    • 1
    • 2
  • Julien Jomier
    • 1
  • Derek Cool
    • 1
  • Stephen Aylward
    • 1
  1. 1.Computer-Aided Diagnosis and Display Lab, Department of RadiologyThe University of North Carolina at Chapel HillUSA
  2. 2.Ecole Superieure de Chimie Physique de LyonLyonFrance

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