11 March 2008 Conditional statistical model building
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We present a new statistical deformation model suited for parameterized grids with different resolutions. Our method models the covariances between multiple grid levels explicitly, and allows for very efficient fitting of the model to data on multiple scales. The model is validated on a data set consisting of 62 annotated MR images of Corpus Callosum. One fifth of the data set was used as a training set, which was non-rigidly registered to each other without a shape prior. From the non-rigidly registered training set a shape prior was constructed by performing principal component analysis on each grid level and using the results to construct a conditional shape model, conditioning the finer parameters with the coarser grid levels. The remaining shapes were registered with the constructed shape prior. The dice measures for the registration without prior and the registration with a prior were 0.875 ± 0.042 and 0.8615 ± 0.051, respectively.
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Mads Fogtmann Hansen, Mads Fogtmann Hansen, Michael Sass Hansen, Michael Sass Hansen, Rasmus Larsen, Rasmus Larsen, } "Conditional statistical model building", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691410 (11 March 2008); doi: 10.1117/12.771079; https://doi.org/10.1117/12.771079

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