Accuracy Estimation for Medical Image Registration Using Regression Forests
This paper reports a new automatic algorithm to estimate the misregistration in a quantitative manner. A random regression forest is constructed, predicting the local registration error. The forest is built using local and modality independent features related to the registration precision, the transformation model and intensity-based similarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans. The results show that the mean absolute error of regression is 0.72 ± 0.96 mm and the accuracy of classification in three classes (correct, poor and wrong registration) is 93.4 %, comparing favorably to a competing method. In conclusion, a method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.
KeywordsImage registration Registration accuracy Uncertainty estimation Regression forests
- 2.Datteri, R.D., Dawant, B.M.: Automatic detection of the magnitude and spatial location of error in non-rigid registration. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds.) WBIR 2012. LNCS, vol. 7359, pp. 21–30. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31340-0_3 CrossRefGoogle Scholar
- 7.Lotfi, T., Tang, L., Andrews, S., Hamarneh, G.: Improving probabilistic image registration via reinforcement learning and uncertainty evaluation. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds.) MLMI 2013. LNCS, vol. 8184, pp. 187–194. Springer, Heidelberg (2013). doi: 10.1007/978-3-319-02267-3_24 CrossRefGoogle Scholar