Medical Image Registration

By B. Naren, M. S. Singh. 2001

1,463 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4d527bc9fe7204c7f14e6b9aa6c707a3e0c9a14c

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Citations in the corpus

Highly Accurate Inverse Consistent Registration: A Robust Approach (2010, methodology): “Over the last 20 years methods for the registration of images (and in particular medical images) have been studied intensely (see e.g. Maintz and Viergever (1998); Maes et al. (1999); Hill et al. (2001) for surveys and comparisons).”
A Review on Medical Image Registration as an Optimization Problem (2017, unknown): “scholars have reviewed the registration in [1-5].”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, background): “A few more general survey papers [19]–[21] may also be of interest to the reader.”
A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations (2001, background): “Even though there is a large number of registration methods to be found in the literature [2,3], existing methods have usually been designed for or fine-tuned to specific, mostly single-modality applications.”
Medical image registration: a review (2014, methodology): “…et al. (2009), Zitova´ and Flusser (2003), medical image registration in general (Elsen et al. 1993; Maintz and Viergever 1998; Bronzino 2000; Hajnal et al. 2001; Hill et al. 2001; Modersitzki 2004; Goshtasby 2005; Fischer and Moder-sitzki 2008; Slomka and Baum 2009) and hierarchical…”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, unknown): “The spatial alignment is typically initialized using rigid or affine transformation [34].”
What is the best similarity measure for motion correction in fMRI time series? (2002, background): “sidering the huge number of different intensity-based similarity measures proposed in the literature [15], [16], we have decided to select only one measure among each different family of the taxonomy recently proposed by Roche [1].”
An approach to multimodal biomedical image registration utilizing particle swarm optimization (2004, background): “These methods still frequently become trapped in local optima, as the global optimum may not be present in lower resolutions [2], [19].”
Non-rigid image registration: theory and practice. (2004, methodology): “For more specific aspects of image registration, the reader is referred to other reviews; there is good technical coverage in Hill et al [1], Brown [15], Lester and Arridge [16], Maintz and Viergever [17] and Zitova and Flusser [18], reviews of cardiac applications in Makela et al [19], nuclear medi”
The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods (2008, unknown): “, 3D gradwarp (19,20); corrections for intensity nonuniformity due to nonuniform receiver coil sensitivity (21); and correction of image intensity nonuniformity due to other causes such as wave effects at 3 T.”
The dual-bootstrap iterative closest point algorithm with application to retinal image registration (2003, background): “Intensity-based approaches generally optimize an objective function based on comparison of intensities or intensity gradients [59], or based on measures such as mutual information [31, 45, 82].”
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation (2015, background): “…is, however, not limited to MNIST: • Image alignment is a routine task in many medical imaging tasks, such as the analysis of magnetic resonance images (MRI) [7, 13, 37], X-ray Computed Tomography (CT) [34, 41], Positron Emission Tomography (PET) [45, 46] and mam-Class-dependent mograms [24, 38].”
Image registration methods: a survey (2003, unknown): “[86] [111] [123] [195].”