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%.
Automatic detection of registration errors for quality assessment in medical image registration
S. Muenzing, K. Murphy, B. van Ginneken and J. Pluim
Medical Imaging 2009;7259:72590K1-72590K9.