A minimum description length approach to statistical shape modeling

By R. Davies, C. Twining, Tim Cootes, J. Waterton, Christopher J. Taylor. IEEE Transactions on Medical Imaging, 2002

639 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/079b89de1a5f71e4035d22e40784937536bc6345

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Citations in the corpus

Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration (2003, methodology): “Recently, Davies et al. [23], [24] proposed a new method for establishing correspondences between shapes by optimizing the quality of the statistical model based on the minimum description length of the resulting model.”
Automatic Model-Based Segmentation of the Heart in CT Images (2008, methodology): “Alternatively, recent approaches based on the minimum description length [43], [44] can be used to derive the correspondence between vertices resulting in more compact shape models.”
3-D active appearance models: segmentation of cardiac MR and ultrasound images (2002, methodology): “This is done by creating corresponding points via a parametric representation of each individual shape and optimizing this function with respect to the compactness of the final shape model [32].”
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability (2006, methodology): “Yet another class of deformable template segmentation methods uses a statistical model of shape and intensity to identify individual anatomical structures (Cootes et al., 1998; Joshi et al., 2002; Davies et al., 2002).”
Integral Invariants for Shape Matching (2006, background): “In medical imaging, a given anatomical structure may be modeled by a statistical shape representation [45], [21].”
Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling (2002, methodology): “[11] published a method to automatically extract a set of optimal landmarks using the minimum description length (MDL) principle.”
Shape Registration with Spherical Cross Correlation (2008, methodology): “Davies [4] has used an information-theoretic framework to formulate shape alignment as a minimum description length (MDL) problem.”
Automatic segmentation of echocardiographic sequences by active appearance motion models (2002, background): “This is an actively researched field both in 2-D and 3-D [24]–[26], where correspondence is established based on shape features.”
Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features (2008, neutral): “Compared to other approaches [18], [19], [29] for establishing point correspondence, our solution is simple and each shape is processed independently.”
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods (2011, background): “The landmarks can be manually placed or automatically determined from a continuous shape e.g., a parametrized curve or a polygonal line in 2D, or a parametrized surface or a binary mask in 3D [ 34 ]‐[37].”