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Evaluation of Brain MRI Alignment with the Robust Hausdorff Distance Measures

 

Andriy Fedorov, Eric Billet, Marcel Prastawa, Alireza Radmanesh, Guido Gerig, Ron Kikinis, Simon K. Warfield and Nikos Chrisochoides.

 

Published in 4th International Symposium on Visual Computing, Publisher Springer, pages 594 -- 603, 2008

 

Abstract

 

We present a novel method for automated assessment of image alignment, with the application to non-rigid registration of brain MRI for image-guided neurosurgery. We propose a number of robust modifications to the Hausdorff Distance (HD) metric, and apply it to the edges recovered from the brain MRI to evaluate the accuracy of image alignment. Based on the evaluation of the proposed technique on the synthetically deformed images, simulated tumor growth MRI and real neurosurgery data with expert-identified anatomical landmarks, the accuracy of the error assessment is significantly improved compared to the conventional HD. The proposed evaluation approach can be used to increase confidence in the registration results, assist in registration parameter selection, and provide local estimates and visual assessment of the registration error.