HAMMER: hierarchical attribute matching mechanism for elastic registration

By Dinggang Shen, Christos Davatzikos. IEEE Transactions on Medical Imaging, 2002

1,190 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/94109e0905520d45c514f8e123a2769321d0f83d

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Citations in the corpus

Power in Voxel-based Lesion-Symptom Mapping (2007, methodology): “A number of automated and semiautomated algorithms are available for MRI data (e.g., Avants, Schoenemann, & Gee, 2005; Shen & Davatzikos, 2002; Ashburner & Friston, 1999; Woods, Grafton, Watson, Sicotte, & Mazziotta, 1998; see also Brett, Johnsrude, & Owen, 2002 for discussion of this issue) and…”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, methodology): “The nonrigid registration algorithms are typically based on either physical models for transformation such as elastic [35] or fluid deformation models [36] or a linear combination of smooth basis functions [37] or free-form deformations [38].”
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (2008, positive): “…(WM), and cerebrospinal ﬂ uid (CSF), using a brain tissue segmentation method proposed in (Pham and Prince, 1999); 4) High-dimensional image warping (Shen and Davatzikos, 2002) to a standardized coordinate system, a brain atlas (template) that was aligned with the MNI coordinate space (Kabani…”
An Unsupervised Learning Model for Deformable Medical Image Registration (2018, methodology): “These include elastic-type models [6, 38], statistical parametric mapping [3], free-form deformations with b-splines, [37] and Demons [42].”
Association of Hearing Impairment with Brain Volume Changes in Older Adults (2014, methodology): “A template-based deformation approach is employed, using the ICBMstandardMRI (Montreal Neurologic Institute) as the template and a hierarchical elastic matching algorithm for ROI determination (Shen and Davatzikos, 2002) (see Fig.”
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (2011, methodology): “After registration using HAMMER (Shen and Davatzikos, 2002), we obtain the subject-labeled image based on a template with 93 manually labeled ROIs (Kabani et al.”
Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis (2015, methodology): “The recent advances in the structural parcellation technique of the entire brain (Shen and Davatzikos, 2002; Shi, 2010; Wang, 2013) now provides tools to structure raw MR images into a standardized and quantitative indices.”
A review of multimodal image matching: Methods and applications (2021, methodology): “Several heuristic and metaheuristic methods, such as greedy [81,82] and evolutionary algorithms [83,84], are also investigated to explore a larger solution space, thus being able to handle a more general problem, but they cannot guarantee their optimal solutions.”
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain (2008, methodology): “Inverse consistency was first introduced by Thirion as an extension to his Demons algorithm (Thirion, 1998) but was popularized by Christensen and Johnson (2001) and others (Shen and Davatzikos, 2002).”
A framework to study the cortical folding patterns. (2004, background): “Most are driven by the maximization of an intensity-based similarity measure computed between the processed image and a template image (Cachier et al., 2003; Guimond et al., 2001; Hellier et al., 2001; Shen and Davatzikos, 2002).”
Construction of a 3D probabilistic atlas of human cortical structures (2008, background): “Multi-subject studies such as the ICBM project require methods that can bring the image data from different subjects into a common coordinate frame; numerous research efforts have been made to meet these demands (e.g., Bajcsy et al., 1983; Gee et al., 1993; Woods et al., 1993, 1998; Collins et al., ”
Deformable Image Registration Using Cue-aware Deep Regression Network (2018, methodology): “Additional properties, such as symmetry [28], [29], can also be imposed.”
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, methodology): “The pre-processing steps included 1) alignment to the AC–PC plane; 2) removal of extra-cranial material (skull-stripping); 3) tissue segmentation into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using a brain tissue segmentation method proposed in Pham and Prince (1999); 4) h”
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome (2011, methodology): “We further assume that images were spatially normalized to a common stereotaxic space (e.g. Shen and Davatzikos, 2002; Ashburner, 2007) as in many group studies or classification methods (Lao et al., 2004; Fan et al., 2007; Klöppel et al., 2008a; Vemuri et al., 2008; Querbes et al., 2009; Cuingnet…”
RABBIT: Rapid Alignment of Brains by Building Intermediate Templates (2009, positive): “Finally, the estimated intermediate deformation field is further refined by a general registration algorithm, namely HAMMER (Shen and Davatzikos, 2002), to register the intermediate template with the individual brain image.”
Survey of Non-Rigid Registration Tools in Medicine (2017, methodology): “4 bUnwarpJ MSD Inverse consistent B-spline [60] 5 DRAMMS CC, SSD Deformable registration via attribute matching and mutual-saliency weighting [62] 6 Drop SAD, SADG, SSD, NCC, NMI, CR, CCGIP, HD, JRD, MI, JE, GRAD Deformable image registration based on Markov random fields and discrete optimization [”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, positive): “(2011) constructed infant atlases for neonates, 1-year-olds, and 2-year-olds from 95 subjects using non-rigid registration (Shen and Davatzikos, 2002).”