2D Image Registration in CT Images Using Radial Image Descriptors

By Franz Graf, Hans-Peter Kriegel, Matthias Schubert, Sebastian Pölsterl, A. Cavallaro. 2011

98 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Medical Imaging

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Citations in the corpus

Decentralized gradient methods: does topology matter? (2020, neutral): “Minimization of mean squared error (MSE) for linear regression on the dataset “Relative location of CT slices on axial axis” from (uci; Graf et al., 2011).”
Supervised Dimensionality Reduction via Distance Correlation Maximization (2016, methodology): “…Forzani, 2009] 26.74 25.57 24.39 24.26 24.20 SAVE [Shao et al., 2009] 28.18 27.82 27.62 27.53 27.50 SIR [Li, 1991] 29.92 29.46 29.18 28.86 28.63 Table 2: Geographical Origin of Music [Graf et al., 2011]: The input contains audio features extracted from 1059 wave ﬁles covering 33 countries/areas.”
Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization (2019, methodology): “We use 4 popular UCI (Lichman, 2013) datasets for regression: protein structure (Rana, 2013), slice localization (Graf et al., 2011), naval propulsion (Coraddu et al., 2014) and parkinsons telemonitoring (Tsanas et al., 2010).”
NAS-Bench-101: Towards Reproducible Neural Architecture Search (2019, methodology): “We use 3 popular UCI (Lichman, 2013) datasets for regression: protein structure (Rana, 2013), slice localization (Graf et al., 2011) and year prediction (Bertin-Mahieux et al., 2011).”
Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018, methodology): “Datasets: We use the following datasets: blog feedback [4], indoor location [43], slice localisation [11], naval propulsion [5], protein tertiary structure [31], news popularity [7], Cifar10 [21].”
DART: Dropouts meet Multiple Additive Regression Trees (2015, methodology): “To test the merits of using dropouts for regression tasks we have used the CT slices dataset (Graf et al., 2011) available at the UCI repository (Bache and Lich-man, 2013).”
The Role of Network Topology for Distributed Machine Learning (2019, methodology): “a) CT: (named “Relative location of CT slices on axial axis” in the repository) has m = 53500 samples each with p = 385 features extracted from computer tomography images [19].”
Sub-sampled Newton Methods with Non-uniform Sampling (2016, unknown): “DATASET CT slices[9] Forest[2] Adult[13] Buzz[11] n 53,500 581,012 32,561 59,535 d 385 55 123 78 κ 368 221 182 37 κ̂ 47,078 322,370 69,359 384,580”
Learning accurate and interpretable models based on regularized random forests regression (2014, methodology): “The Relative location of computed tomography (CT) slices on axial axis data set [14] consists of 384 features extracted from CT images.”