Sparse Modeling of Landmark and Texture Variability using the Orthomax Criterion

Mikkel Bille Stegmann, Karl Sjöstrand, Rasmus Larsen

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    In the past decade, statistical shape modeling has been widely popularized in the medical image analysis community. Predominantly, principal component analysis (PCA) has been employed to model biological shape variability. Here, a reparameterization with orthogonal basis vectors is obtained such that the variance of the input data is maximized. This property drives models toward \$\backslash\$textit{global} shape deformations and has been highly successful in fitting shape models to new images. However, recent literature has indicated that this uncorrelated basis may be suboptimal for exploratory analyses and disease characterization. This paper explores the orthomax class of statistical methods for transforming variable loadings into a \$\backslash\$textit{simple structure} which is more easily interpreted by favoring sparsity. Further, we introduce these transformations into a particular framework traditionally based on PCA; the Active Appearance Models (AAMs). We note that the orthomax transformations are independent of domain dimensionality (2D/3D etc.) and spatial structure. Decompositions of both shape and texture models are carried out. Further, the issue of component ordering is treated by establishing a set of relevant criteria. Experimental results are given on chest radiographs, magnetic resonance images of the brain, and face images. Since pathologies are typically spatially localized, either with respect to shape or texture, we anticipate many medical application areas where sparse parameterizations are preferable to the conventional global PCA approach.
    Original languageEnglish
    Title of host publicationInternational Symposium on Medical Imaging 2006, San Diego, CA
    PublisherThe International Society for Optical Engineering (SPIE)
    Publication date2006
    Publication statusPublished - 2006

    Cite this

    Stegmann, M. B., Sjöstrand, K., & Larsen, R. (2006). Sparse Modeling of Landmark and Texture Variability using the Orthomax Criterion. In International Symposium on Medical Imaging 2006, San Diego, CA The International Society for Optical Engineering (SPIE).