Evaluation of Brain MRI Alignment with the Robust Hausdorff Distance Measures

Authors:
Andriy Fedorov;Eric Billet;Marcel Prastawa;Guido Gerig;Alireza Radmanesh;Simon K. Warfield;Ron Kikinis;Nikos Chrisochoides
Affiliations:
Center for Real-Time Computing, College of William and Mary, USA;Center for Real-Time Computing, College of William and Mary, USA;Scientific Computing and Imaging Institute, University of Utah, USA;Scientific Computing and Imaging Institute, University of Utah, USA;Surgical Planning Laboratory, Harvard Medical School, USA;Computational Radiology Laboratory, Harvard Medical School, USA;Surgical Planning Laboratory, Harvard Medical School, USA;Center for Real-Time Computing, College of William and Mary, USA
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

Citing 9
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression and outlier detection

Robust regression and outlier detection
Comparing Images Using the Hausdorff Distance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Point similarity measures for non-rigid registration of multi-modal data

Computer Vision and Image Understanding
Binary-image comparison with local-dissimilarity quantification

Pattern Recognition
A new Hausdorff distance for image matching

Pattern Recognition Letters
Non-rigid registration of pre-procedural MR images with intra-procedural unenhanced CT images for improved targeting of tumors during liver radiofrequency ablations

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Synthetic ground truth for validation of brain tumor MRI segmentation

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Introduction to the non-rigid image registration evaluation project (NIREP)

WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel automated method for assessment of image alignment, applied to non-rigid registration of brain Magnetic Resonance Imaging data (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. The evaluation results on synthetic images, simulated tumor growth MRI and real neurosurgery data with expert-identified anatomical landmarks, confirm that the accuracy of alignment error estimation is improved compared to the conventional HD. The proposed 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.