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9 March 2010 Hierarchical patch generation for multilevel statistical shape analysis by principal factor analysis decomposition
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Abstract
We present a framework for multi-level statistical shape analysis, applied to the study of anatomical variability of abdominal organs. Statistical models were built hierarchically, allowing the representation of different levels of detail. Principal factor analysis was used for decomposition of deformation fields obtained from non-rigid registration at different levels, and provided a compact model to study shape variability within the abdomen. To assess and ease the interpretability of the resulting deformation modes, a clustering technique of the deformation vectors was proposed. The analysis of deformation fields showed a strong correlation with anatomical landmarks and known mechanical deformations in the abdomen. Clusters of modes of deformation from fine-to-coarse levels explain tissue properties, and inter-organ relationships. Our method further presents the automated hierarchical partitioning of organs into anatomically significant components that represent potentially important constraints for abdominal diagnosis and modeling, and that may be used as a complement to multi-level statistical shape models.
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Mauricio Reyes, Miguel A. González Ballester, Nina Kozic, Ronald M. Summers, and Marius George Linguraru "Hierarchical patch generation for multilevel statistical shape analysis by principal factor analysis decomposition", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 762617 (9 March 2010); https://doi.org/10.1117/12.843253
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