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Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration
Medical Image Analysis, 08/01/2012
Muenzing SEA et al. – The authors employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single–feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi–feature model is validated against the set of single–feature classifiers. They consider the setup generic, however, its application is demonstrated on 51 CT follow–up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%.