Survey of Medical Image Registration

By Vaidy Mani, Dr.S. rivazhagan. 2013

1,151 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A survey of MRI-based brain tumor segmentation methods (2014, methodology): “Atlas-based algorithm was ﬁrstly introduced to register different images [74] .”
Image Registration Techniques: A Survey (2017, background): “Image registration techniques can be classified based on some criteria [44-45].”
Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise (2017, methodology): “High-res anatomical images were coregistered to the functional scans, all data were subsequently normalized to the Montreal Neurological Institute (MNI) template (cf. [47]).”
Machine Learning and Radiology (2012, background): “, 2003); probabilistic models provide a theoretical framework for medical image analysis, such as image reconstruction (Levitan and Herman, 1987), segmentation (Zhang and Goldman, 2001), and registration (Ashburner et al., 1997; Maintz and Viergever, 1998).”
A survey of GPU-based medical image computing techniques. (2012, methodology): “Starting from an initial guess, () t T ⋅ converges to the optimum in a series of iterative steps depending on the corresponding objective function, image transformations and optimization technique (31,74).”
Medical image registration: a review (2014, methodology): “…in Brown (1992), Salvi et al. (2007), Wyawahare 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;…”
Brain templates and atlases (2012, background): “Over the last 15 years, numerous comparison studies have been performed to assess the relative performance of different spatial normalization algorithms (e.g., Ghosh et al., 2010; Hellier et al., 2003; Klein et al., 2009; Maintz and Viergever, 1998).”
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge (2011, neutral): “These consisted of 4 scan pairs in which the images were related by artiﬁcial warping, and 4 scan pairs from ovine data where ﬁducial marker locations were known.”
Medical Image Segmentation Methods, Algorithms, and Applications (2014, methodology): “Many techniques have been developed based on X-ray and cross-sectional images like Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), or other tomographic modalities (SPECT, PET, or ultrasound) [2,3].”
A review of multimodal image matching: Methods and applications (2021, background): “Readers can refer to [29,75,76] for more details and evaluations.”
A Review of Point Cloud Registration Algorithms for Mobile Robotics (2015, methodology): “The field of registration crystalized with its first survey on medical image registration covering the years 1993 to 1998 [Maintz and Viergever, 1998].”
Deformable Image Registration Using Cue-aware Deep Regression Network (2018, background): “Comprehensive summaries of registration methods can be found in [12]–[19].”
A review of 3D/2D registration methods for image-guided interventions (2012, methodology): “The term 3D/2D image-to-image registration can refer either to alignment of 3D spatial data to a single tomographic slice or to alignment of 3D spatial data to projective data (Maintz and Viergever, 1998).”
A survey of medical image registration - under review (2016, positive): “The major problems identiﬁed in ( Maintz and Viergever, 1998 ) were not related to the methodology of registration, but rather to the situation after a registration has been obtained.”