The dual-bootstrap iterative closest point algorithm with application to retinal image registration

By Charles V. Stewart, Chia-Ling Tsai, B. Roysam. IEEE Transactions on Medical Imaging, 2003

410 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5ef20baee2fd1b22a97c39d120ed29694e6f7113

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Review on Medical Image Registration as an Optimization Problem (2017, background): “There are some examples as global-to-local matching [43], hierarchical model refinement [44], and dual-bootstrap [45].”
Medical image registration: a review (2014, methodology): “For the iterative matching optimisation, besides the optimisation algorithms previously indicated, common algorithms are the ICP (Besl and McKay 1992) and its variations (Stewart et al. 2003; Andreetto et al. 2004; Giessen et al. 2009; Tsai et al. 2010; Pan et al. 2011).”
A review of biometric technology along with trends and prospects (2014, methodology): “Table 3: Summary of ocular region modalities: Modality Feature Set Recognition Techniques Datasets Iris Color, shape and iris texture (crypts, furrows, corona, freckles) 2D Gabor filters [144, 154, 155], Wavelets [156-159], LoG filter [160], DCT [161], Ordinal measures [162, 163], ICA [164], PCA [16”
Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures (2006, background): “Second, these centerline points, sometimes together with approximate widths, are what is most needed in algorithms such as registration [12] and optic disc detection [6] that depend on vessel extraction.”
Image change detection algorithms: a systematic survey (2005, background): “Several modern registration algorithms are capable of switching automatically to higher order transformations after being initialized with a low-order similarity transformation [33].”
Static and dynamic 3D facial expression recognition: A comprehensive survey (2012, background): “Many variants of ICP have also been proposed , and the interested reader may refer to the following [70] [71] [72].”
A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration (2010, positive): “2) Comparative Results: Herein comparative experiments between the Harris-PIIFD, SIFT, and GDB-ICP are implemented .”
Retinal Imaging and Image Analysis (2010, neutral): “Examples of feature-based registration are global-to-local matching [175], hierarchical model refinement [176], and dual-bootstrap [177].”
A Review of Point Cloud Registration Algorithms for Mobile Robotics (2015, methodology): “) In the current literature reviewed, other distances used in matching functions were Mahalanobis [Stewart et al., 2003] and χ2-test statistic [Tsai et al.”
Survey of Medical Image Registration (2013, methodology): “V. Stewart et al used dual-bootstrap iterative closest point algorithm to register retinal images [154].”
Deformable Medical Image Registration: A Survey (2013, methodology): “[241] proposed a dual-bootstrap ICP method to register retinal images.”