Statistical shape analysis via principal factor analysis

Reyes, Mauricio; Linguraru, Marius George; Marias, Kostas; Ayache, Nicholas; Nolte, Lutz-Peter; Gonzalez Ballester, Miguel Angel (2007). Statistical shape analysis via principal factor analysis. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007 (pp. 1216-1219). Washington DC, USA: IEEE 10.1109/ISBI.2007.357077

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Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Item Type:

Conference or Workshop Item (Paper)


04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]

UniBE Contributor:

Reyes, Mauricio; Nolte, Lutz-Peter and Gonzalez Ballester, Miguel Angel


500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health








Factscience Import

Date Deposited:

04 Oct 2013 14:57

Last Modified:

29 Dec 2014 08:41

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URI: (FactScience: 47433)

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