Robust Global Registration through Geodesic Paths on an Empirical Manifold with Knee MRI from the Osteoarthritis Initiative (OAI)
Accurate affine registrations are crucial for many applications in medical image analysis. Within the Osteoarthritis Initiative (OAI) dataset we have observed a failure rate of approximately 4% for direct affine registrations of knee MRI without manual initialisation. Despite this, the problem of robust affine registration has not received much attention in recent years. With the increase in large medical image datasets, manual intervention is not a suitable solution to achieve successful affine registrations. We introduce a framework to improve the robustness of affine registrations without prior manual initialisations. We use 10,307 MR images from the large dataset available from the OAI to model the low dimensional manifold of the population of unregistered knee MRIs as a sparse k-nearest-neighbour graph. Affine registrations are computed in advance for nearest neighbours only. When a pairwise image registration is required the shortest path across the graph is extracted to find a geodesic path on the empirical manifold. The precomputed affine transformations on this path are composed to find an estimated transformation. Finally a refinement step is used to further improve registration accuracy. Failure rates of geodesic affine registrations reduce to 0.86% with the registration framework proposed.
KeywordsIterative Close Point Geodesic Path Manifold Learning Medical Image Analysis Local Registration
Unable to display preview. Download preview PDF.
- 1.Tamez-Pena, J., Gonzalez, P., Farber, J., Baum, K., Schreyer, E., Totterman, S.: Atlas based method for the automated segmentation and quantification of knee features: Data from the osteoarthritis initiative. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1484–1487 (2011)Google Scholar
- 5.Donoghue, C., Rao, A., Bull, A.M.J., Rueckert, D.: Manifold learning for automatically predicting articular cartilage morphology in the knee with data from the osteoarthritis initiative (oai). In: SPIE Medical Imaging 2011: Image Processing, Proc., vol. 7962, p. 12 (2011)Google Scholar
- 7.Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Physics in Medicine and Biology 46(3), R1–R45 (2001)Google Scholar
- 16.Indyk, P., Motwani, R.: Approximate nearest neighbors: Towards removing the curse of dimensionality. In: STOC 1998 Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604–613 (1998)Google Scholar