Performance Evaluation of Grid-Enabled Registration Algorithms Using Bronze-Standards

Evaluating registration algorithms is difficult due to the lack of gold standard in most clinical procedures. The bronze standard is a real-data based statistical method providing an alternative registration reference through a computationally intensive image database registration procedure. We propose in this paper an efficient implementation of this method through a grid-interfaced workflow enactor enabling the concurrent processing of hundreds of image registrations in a couple of hours only. The performances of two different grid infrastructures were compared. We computed the accuracy of 4 different rigid registration algorithms on longitudinal MRI images of brain tumors. Results showed an average subvoxel accuracy of 0.4 mm and 0.15 degrees in rotation.

[1]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[2]  Matthew R. Pocock,et al.  Taverna: a tool for the composition and enactment of bioinformatics workflows , 2004, Bioinform..

[3]  Sébastien Ourselin,et al.  Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images , 2000, MICCAI.

[4]  N. Ayache,et al.  Landmark-based registration using features identified through differential geometry , 2000 .

[5]  Branislav Jaramaz,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000 , 2000, Lecture Notes in Computer Science.

[6]  David J. Hawkes,et al.  Voxel Similarity Measures for 3D Serial MR Brain Image Registration , 2000, IEEE Trans. Medical Imaging.

[7]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[8]  Ramin Shahidi,et al.  Validation of medical image processing in image-guided therapy , 2002, IEEE Transactions on Medical Imaging.

[9]  D. Louis Collins,et al.  Retrospective evaluation of intersubject brain registration , 2003, IEEE Transactions on Medical Imaging.

[10]  Nicholas Ayache,et al.  Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information , 2001, IEEE Transactions on Medical Imaging.

[11]  Xavier Pennec,et al.  Feature-Based Registration of Medical Images: Estimation and Validation of the Pose Accuracy , 1998, MICCAI.

[12]  Johan Montagnat,et al.  Efficient services composition for grid-enabled data-intensive applications , 2006, 2006 15th IEEE International Conference on High Performance Distributed Computing.

[13]  Xavier Pennec,et al.  Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements , 2006, Journal of Mathematical Imaging and Vision.