A Binary Entropy Measure to Assess Nonrigid Registration Algorithms
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
A shape-based approach to robust image segmentation
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Efficient population registration of 3d data
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
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Two key aspects of coupled multi-object shape analysis are the choice of representation and subsequent registration to align the sample set. Current techniques for such analysis tend to trade off performance between the two tasks, performing well for one task but developing problems when used for the other. This article proposes Ln label space, a representation that is both flexible and well suited for both tasks. We propose to map object labels to vertices of a regular simplex, e.g. the unit interval for two labels, a triangle for three labels, a tetrahedron for four labels, etc. This forms a linear space with the property that all labels are equally separated. On examination, this representation has several desirable properties: algebraic operations may be done directly, label uncertainty is expressed as a weighted mixture of labels, interpolation is unbiased toward any label or the background, and registration may be performed directly. To demonstrate these properties, we describe variational registration directly in this space. Many registration methods fix one of the maps and align the rest of the set to this fixed map. To remove the bias induced by arbitrary selection of the fixed map, we align a set of label maps to their intrinsic mean map.