Image registration by maximization of combined mutual information and gradient information

By Josien P. W. Pluim, J. B. Antoine Maintz, Max A. Viergever. IEEE Transactions on Medical Imaging, 2000

818 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0f097bf054d522792383e365efe3d59ad974f01f

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Unseen Class Discovery in Open-world Classification (2018, methodology): “…with these images’ ground-truth labels using the popular evaluation metric (we re-deﬁned some notations here): Normalized Mutual Information (NMI) (Pluim et al., 2000), which is a normalization of the Mutual Information (MI) to scale the results to between 0 (no mutual information) and 1 (perfect…”
A New Method for Improving Functional-to-Structural MRI Alignment using Local Pearson Correlation (2008, methodology): “To carry out the minimization of C[E, S] over the parameter vector θ, our software incorporates Powell's derivative-free NEWUOA software (Powell, 2006), modified slightly to allow for range constraints on the solution parameters.”
Deformable Medical Image Registration: A Survey (2013, methodology): “[351] used the intensity image gradient as an ad-”
A Viscous Fluid Model for Multimodal Non-rigid Image Registration Using Mutual Information (2002, background): “…reflected by their joint intensity histogram, which varies as sponding anatomical landmarks or object surfaces in the the registration parameters are changed (M es et al., 1997; images to be registered, which is non trivial in most Wells et al., 1996; Studholme et al., 1999; Pluim et al., 2000).”
Image Similarity Using Mutual Information of Regions (2004, methodology): “Both [8] and [9] report improved robustness of their metrics over standard mutual information.”
Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory (2005, methodology): “PET-MR alignment was performed using cross-modal registration maximizing alignment of intensity gradients taking into account relative gradient orientation (Pluim et al., 2000).”
Mutual-information-based registration of medical images: a survey (2003, methodology): “To conclude, we present a list of papers that we did not consider true comparison studies, but that do contain comparisons between mutual information and other measures [12], [29], [44], [46], [51]–[53], [64], [69], [89], [91], [93], [95], [102], [103], [107], [127], [128], [130], [152], [156].”
Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity (2017, background): “The similarity curve can qualitatively analyze the matching performance of similarity metrics [47].”
A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume (2004, neutral): “hi) of an angular factor (Pluim et al., 2000) that is maximal when gradients are either aligned or anti-aligned.”
Fast and Robust Matching for Multimodal Remote Sensing Image Registration (2019, methodology): “More-over, some state-of-the-art similarity measures, such as MI, GMI [43], HOG ncc , HOPC ncc [34], and modality independent neighborhood descriptor (MIND) [37], are used for comparisons.”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, background): “A combination of gradient information and MI has been examined in [122].”
Medical image registration: a review (2014, methodology): “…identifying a set of corresponding points in both input images, e.g. ﬁducial markers placed into the patients or the organs, and use them to assess the registration accuracy (Collignon et al. 1997; Maes et al. 1997; West et al 1997, 1999; Penney et al. 1998; Pluim et al. 2000; Mattes et al. 2003).”
Medical image registration using mutual information (2003, background): “[84], spatial information is incorporated in the registration criterion by multiplying MI with a gradient term that measures the coincidence of voxels in both images with strong gradients of similar orientation, which is considered to be indicative of alignment of tissue transitions in both images.”
An approach to multimodal biomedical image registration utilizing particle swarm optimization (2004, methodology): “Thus, in addition to exercising care when selecting an initial orientation [7], other features, such as intensity gradient information, should also be utilized [ 15 ].”
Fast parametric elastic image registration (2003, background): “based similarity measures, such as information-based measures [41], especially mutual information [17], or weightednorms.”