Mutually coherent structural representation for image registration through joint manifold embedding and alignment

In this paper, we introduce mutually coherent structural representations (McSR) for image registration which learns a mapping of local structural descriptors to create a unique scalar representation, which is similar across modalities. The McSR is learnt using joint alignment and embedding of Laplacian eigenmaps using modality-combination specific dense structural descriptors. The resulting alternate image representation offers richer structurally-driven information for registration while being invariant to inter-modal differences in intensities. The proposed formulation has been evaluated for robustness and registration error on standard multimodal brain image datasets. It is observed to demonstrate superior systematic recovery and performance over comparative simultaneous registration methods.

[1]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Michael Brady,et al.  MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration , 2012, Medical Image Anal..

[3]  Jan Modersitzki,et al.  Curvature Based Image Registration , 2004, Journal of Mathematical Imaging and Vision.

[4]  Nathan D. Cahill,et al.  Normalized Measures of Mutual Information with General Definitions of Entropy for Multimodal Image Registration , 2010, WBIR.

[5]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[6]  Nassir Navab,et al.  Manifold Learning for Multi-Modal Image Registration , 2010, BMVC.

[7]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[8]  Matti Pietikäinen,et al.  Discriminative features for texture description , 2012, Pattern Recognit..

[9]  Nassir Navab,et al.  Entropy and Laplacian images: Structural representations for multi-modal registration , 2012, Medical Image Anal..

[10]  Chang Wang,et al.  A General Framework for Manifold Alignment , 2009, AAAI Fall Symposium: Manifold Learning and Its Applications.

[11]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[12]  A. Evans,et al.  MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.