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

Sascha E. A. Muenzing; Keelin Murphy; Bram van Ginneken; Josien P. W. Pluim

doi:10.1117/12.812659

27 March 2009 Automatic detection of registration errors for quality assessment in medical image registration

Sascha E. A. Muenzing, Keelin Murphy, Bram van Ginneken, Josien P. W. Pluim

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72590K (2009) https://doi.org/10.1117/12.812659
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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%.

Citation Download Citation

Sascha E. A. Muenzing, Keelin Murphy, Bram van Ginneken, and Josien P. W. Pluim "Automatic detection of registration errors for quality assessment in medical image registration", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590K (27 March 2009); https://doi.org/10.1117/12.812659

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