Automatic detection of registration errors for quality assessment in medical image registration

S.E.A. Muenzing, K. Murphy, B. Van Ginneken, J.P.W. Pluim

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)


A novel method for quality assessment in medical image registration is presented. It is evaluated on 24 follow-up CT scan pairs of the lung. Based on a reference standard of manually matched landmarks we established a pattern recognition approach for detection of local registration errors. To capture characteristics of these misalignments a set of intensity, entropy and deformation related features was employed. Feature selection was conducted and a kNN classifier was trained and evaluated on a subset of landmarks. Registration errors larger than 2 mm were classified with a sensitivity of 88% and specificity of 94%.

Original languageEnglish
Title of host publicationProgress in Biomedical Optics and Imaging
Publication statusPublished - 2009
Externally publishedYes
Event2009 Medical Imaging : Image Processing - Disney Coronado Springs Resort, Lake Buena Vista, United States
Duration: 7 Feb 200912 Feb 2009

Publication series

NameProceedings of SPIE


Conference2009 Medical Imaging : Image Processing
Country/TerritoryUnited States
CityLake Buena Vista


  • CT
  • Lungs
  • Nonrigid registration
  • Pattern recognition
  • Registration error


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