Multiscale MRF optimization for robust registration of 2D biological data

Discrete formulations of image registration offer the promise of dense deformations via optimizations robust to large motions or poor initialization. However, many available efficient algorithms are not well suited to medical or biological data. We propose a novel multiscale Markov Random Field formulation for image registration, which reduces the number of labels needed at each scale while preserving the ability to represent dense, fine-grained feature matches. The multiscale nature of the algorithm also allows arbitrary sub-voxel accuracy, and we further propose a simple extension which grants a measure of rotational invariance to an arbitrary feature matching term.

[1]  Johannes E. Schindelin,et al.  TrakEM2 Software for Neural Circuit Reconstruction , 2012, PloS one.

[2]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields , 2006, ECCV.

[3]  Vladimir Kolmogorov,et al.  Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Carlos Ortiz-de-Solorzano,et al.  Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization , 2006, CVAMIA.

[5]  Pramode K. Verma,et al.  Medical Image registration using sparse coding and belief propagation , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Narendra Ahuja,et al.  A constant-space belief propagation algorithm for stereo matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Pedro F. Felzenszwalb,et al.  Efficient belief propagation for early vision , 2004, CVPR 2004.

[9]  Nikos Komodakis,et al.  Fast, Approximately Optimal Solutions for Single and Dynamic MRFs , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Václav Hlavác,et al.  Efficient MRF Deformation Model for Non-Rigid Image Matching , 2007, CVPR.

[11]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Ben Glocker,et al.  Deformable medical image registration: setting the state of the art with discrete methods. , 2011, Annual review of biomedical engineering.

[13]  Michael Brady,et al.  Textural mutual information based on cluster trees for multimodal deformable registration , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[14]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..