Volumetric image alignment has been the focus of an important body of research for several decades, and a number of solutions now exist that are effective for analysis of well-curated data.
#E xs max the talos principle images registration#
Alignment is typically addressed via image registration or model fitting methods, where an optimal spatial transform is determined between an image and a template ( Collins et al., 1994 Ourselin et al., 2001 Jenkinson and Smith, 2001 Avants et al., 2008 Klein et al., 2010) or model ( Cootes et al., 2001 Criminisi et al., 2009 Tao et al., 2011), based on a combination of image similarity and geometrical constraints. For example, comparative studies of biomedical image data often require aligning images of different subjects to a normative atlas ( Li et al., 2003 Baiker et al., 2010), and model-to-image alignment can serve as the basis for volumetric object identification ( Criminisi et al., 2009 Flitton et al., 2010) or computer-assisted diagnosis ( Kloppel et al., 2008 Toews et al., 2010). CT volumes of the human body or MR images of the human brain, to a standard frame of reference or model. Many medical imaging applications involve aligning 3D volumetric images, e.g. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment.
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