Statistical shape influence in geodesic active contours

By Michael E. Leventon, W. Grimson, Olivier D. Faugeras. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), 2000

1,152 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/12fe062c314bf4ffa1a9b5e673292d29c7e97553

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape (2007, methodology): “[54] who propose to perform principal component analysis on a set of signed distance function embedding a set of sample shapes.”
A survey of graph theoretical approaches to image segmentation (2013, methodology): “Incorporation of shape prior knowledge has also been extensively studied in the active contour framework, with the statistical shape model [122,123], the global shape model [124] and the shape of interest [125,126].”
Comparison of quantitative methods for cell‐shape analysis (2007, methodology): “…analysis, PCA has been applied to 2-and 3D data sets of the shapes of various anatomical structures (represented as either signed distance maps (Leventon et al. , 2000; Tsai et al. , 2003) or sets of ‘landmark’ points known to correspond to one another (Cootes et al. , 1993; Baumberg & Hogg,…”
Shape Priors for Level Set Representations (2002, methodology): “Then, the shape prior term can account for a large variety of global transformations (opposite to [10]) as well as scale variations (opposite to [ 10 ,21]) and can deal with important local shape variations (opposite to [4]).”
Edge and line oriented contour detection: State of the art (2011, background): “The idea is to add to E a term Eshape that privileges shape similarity between C and a set of training objects [216,217].”
A Bayesian model for joint segmentation and registration (2006, background): “(12) has been discussed in detail in the literature (Wells et al., 1996; Leventon, 2000; Kapur, 1999; Marroquin et al., 2003; Ashburner and Friston, 2005), the next section focuses on determining the registration parameters of Eq.”
Implicit Active Contours Driven by Local Binary Fitting Energy (2007, background): “Second, various prior knowledge, such as shape and intensity distribution, can be easily incorporated into active contour models for robust image segmentation [3, 8, 16].”
Star Shape Prior for Graph-Cut Image Segmentation (2008, methodology): “Using shape priors to improve segmentation has been investigated in the level set and curve evolution frameworks [18–21].”
Deformable Models (2009, background): “Another important area in which Deformable Models have found application is in medical image analysis, most importantly medical image segmentation, see [12], Figure 2 for instance.”
Minimization of Region-Scalable Fitting Energy for Image Segmentation (2008, positive): “Second, active contour models can be easily formulated under a principled energy minimization framework, and allow incorporation of various prior knowledge, such as shape and intensity distribution, for robust image segmentation [5], [14].”
State-of-the-Art Methods for Brain Tissue Segmentation: A Review (2017, background): “tistical prior models are limited by the segmentation accuracy [34], [55].”
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review (2002, methodology): “in [135], whose solution using nite di erence was:”
A level set approach for shape-driven segmentation and tracking of the left ventricle (2003, background): “At the same time, accounting for prior shape knowledge - a significant limitation - was recently addressed in various forms [2], [ 7 ], [13].”
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation (2002, methodology): “Also the incorporation of shape priors to the model is a challenging perspective (Leventon et al., 2000; Chen et al., 2001; Rousson, 2001).”
An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, methodology): “[41] have computed the shape prior by applying principle component analysis (PCA) to the signed distance functions of”
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images (2020, background): “…(Štern et al., 2011; Ibragimov et al., 2014, 2017), deformed with Markov random fields (MRF) (Kadoury et al., 2011, 2013), statistical shape models (Rasoulian et al., 2013; Pereañez et al., 2015; Castro-Mateos et al., 2015), and active contours (Leventon et al., 2002; Athertya & Kumar, 2016).”