@inproceedings{20ba7646a5e44f78b688aab86aa6ccc0,
title = "Automatic detection of registration errors for quality assessment in medical image registration",
abstract = "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\%.",
keywords = "CT, Lungs, Nonrigid registration, Pattern recognition, Registration error",
author = "S.E.A. Muenzing and K. Murphy and \{Van Ginneken\}, B. and J.P.W. Pluim",
year = "2009",
doi = "10.1117/12.812659",
language = "English",
isbn = "9780819475107",
series = "Proceedings of SPIE",
booktitle = "Progress in Biomedical Optics and Imaging",
note = "SPIE Medical Imaging 2009 ; Conference date: 07-02-2009 Through 12-02-2009",
}