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Proceedings Paper

Automatic detection of registration errors for quality assessment in medical image registration
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Paper 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%.

Paper Details

Date Published: 27 March 2009
PDF: 9 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590K (27 March 2009); doi: 10.1117/12.812659
Show Author Affiliations
Sascha E. A. Muenzing, Univ. Medical Ctr. Utrecht (Netherlands)
Keelin Murphy, Univ. Medical Ctr. Utrecht (Netherlands)
Bram van Ginneken, Univ. Medical Ctr. Utrecht (Netherlands)
Josien P. W. Pluim, Univ. Medical Ctr. Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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