Robust Supervoxel Matching Combining Mid-Level Spectral and Context-Rich Features
This paper presents an innovative way to reach accurate semi-dense registration between images based on robust matching of structural entities. The proposed approach relies on a decomposition of images into visual primitives called supervoxels generated by aggregating adjacent voxels sharing similar characteristics. Two new categories of features are estimated at the supervoxel extent: mid-level spectral features relying on a spectral method applied on supervoxel graphs to capture the non-linear modes of intensity displacements, and mid-level context-rich features describing the broadened spatial context on the resulting spectral representations. Accurate supervoxel pairings are established by nearest neighbor search on these newly designed features. The effectiveness of the approach is demonstrated against state-of-the-art methods for semi-dense longitudinal registration of abdominal CT images, relying on liver label propagation and consistency assessment.
KeywordsSemi-dense image registration Supervoxel matching Mid-level representation Laplacian graph Spectral decomposition Context-rich features
This work was partly funded by France Life Imaging (grant ANR-11-INBS-0006 from Investissements d’Avenir program). We acknowledge Visible Patient, www.visiblepatient.com, for 3D liver segmentation masks.
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