Multi‐modal robust inverse‐consistent linear registration. Issue 4 (2nd December 2014)

• Record Type: Journal Article
• Authors: Wachinger, Christian; Golland, Polina; Magnain, Caroline; Fischl, Bruce; Reuter, Martin
• Is Part Of: Human brain mapping. Volume 36:Issue 4(2015:Apr.)
• Page Start: 1365
• Page End: 1380
• Publication Date: 2014-12-02
• Subjects: computer‐assisted image analysis -- rigid and affine image alignment -- statistical model -- magnetic resonance imaging -- optical coherence tomography -- histology; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.22707
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse‐consistent technique for cross‐modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss–Newton update and show that it is equivalent to the efficient second‐order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images. Hum Brain Mapp 36:1365–1380, 2015 . ©2014 Wiley Periodicals, Inc .