Automated Segmentation of Cerebral Aneurysms Based on Conditional Random Field and Gentle Adaboost

  • Hong Zhang
  • Yuanfeng Jiao
  • Yongjie Zhang
  • Kenji Shimada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7599)

Abstract

Quantified geometric characteristics of cerebral aneurysms such as volume, height, maximum diameter, surface area and aspect ratio are useful for predicting the rupture risk. Moreover, a newly developed fluid structure interaction system requires healthy models generated from the aneurysms to calculate anisotropic material directions for more accurate wall stress estimation. Thus the isolation of aneurysms is a critical step which currently depends primarily on manual segmentation. We propose an automated solution to this problem based on conditional random field and gentle adaboost. The proposed method was validated with eight datasets and four-fold cross-validation, an accuracy of 89.63%±3.09% is obtained.

Keywords

Cerebral Aneurysm Segmentation Isolation Conditional Random Field Gentle Adaboost 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hong Zhang
    • 1
  • Yuanfeng Jiao
    • 2
  • Yongjie Zhang
    • 1
    • 2
  • Kenji Shimada
    • 1
    • 2
  1. 1.Department of Mechanical EngineeringCarnegie Mellon UniversityUSA
  2. 2.Department of Biomedical EngineeringCarnegie Mellon UniversityUSA

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