2D Image Registration in CT Images Using Radial Image Descriptors

Graf, Franz; Kriegel, Hans‐Peter; Schubert, Matthias; Pölsterl, Sebastian; Cavallaro, Alexander

doi:10.1007/978-3-642-23629-7_74

Cited by 50 publications

(26 citation statements)

References 6 publications

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“…• The Relative location of CT slices on axial axis data set (referred to as CT data set further on), containing features extracted from CT images [43,37,44].…”

Section: Simulation Setupmentioning

confidence: 99%

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Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified communication

Fodor

Jakovetić

Krejić

et al. 2020

Preprint

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There has been significant interest in distributed optimization algorithms, motivated by applications in Big Data analytics, smart grid, vehicle networks, etc. While there have been extensive theory and theoretical advances, a proportionally small body of scientific literature focuses on numerical evaluation of the proposed methods in actual practical, parallel programming environments. This paper considers a general algorithmic framework of first and second order methods with sparsified communications and computations across worker nodes. The considered framework subsumes several existing methods. In addition, a novel method that utilizes unidirectional sparsified communications is introduced and theoretical convergence analysis is also provided. Namely, we prove R-linear convergence in the expected norm. A thorough empirical evaluation of the methods using Message Passing Interface (MPI) on a High Performance Computing (HPC) cluster is carried out and several useful insights and guidelines on the performance of algorithms and inherent communication-computational trade-offs in a realistic setting are derived.

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“…• The Relative location of CT slices on axial axis data set (referred to as CT data set further on), containing features extracted from CT images [43,37,44].…”

Section: Simulation Setupmentioning

confidence: 99%

“…• the UCI Machine Learning repository, [http://archive.ics.uci.edu/ml] [37] (Gisette [36,38], YearPredictionMSD [39,40], CT [43,44]…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified communication

Fodor

Jakovetić

Krejić

et al. 2020

Preprint

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“…For the experiences, M5P was used to analysis the Relative location of CT slices on axial axis dataset, which can be obtained at UCI Data Mining Repository (Bache and Lichman 2013). In Graf et al (2011b), this dataset was used to predict the relative location of CT (Computed Tomography) slices on the axial axis using k-nearest neighbor search. Also Graf et al (2011a) used the data to apply weighted combinations of image features for the localization of small sub volumes in CT scans.…”

Section: Evaluating Multi-core and Gpu Implementationsmentioning

confidence: 99%

Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls

Engel

Charão

Kirsch-Pinheiro

et al. 2015

J Ambient Intell Human Comput

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International audienceData mining tools may be computationally demanding, which leads to an increasing interest on par- allel computing strategies in order to improve their per- formance. While multi-core processors and Graphics Processing Units (GPUs) accelerators increased the com- puting power of current desktop computers, we observe that desktop-based data mining tools do not take full advantage of these architectures yet. This paper investi- gates strategies to improve the performance of Weka, a popular data mining tool, through multi-core and GPU acceleration. Using performance profiling of Weka, we identify operations that could improve the data mining performance when parallelized. We selected two of these operations, and analyze the impact of their parallel exe- cution on Weka’s performance. These experiments demonstrate that while significant speedups can be achieved, all operations are not prone to be parallelized, which reinforces the need for a careful and well-studied selection of the candidates

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“…The response variable is relative location of an image on the axial axis, which was constructed by manually annotating up to 10 different distinct landmarks in each CT volume with known location. More detailed description of the dataset can be found in Graf et al (2011). Among those 225 images, 200 We use linear regression to analyze the relationship between the feature vector and the response.…”

Section: A Real Data Examplementioning

confidence: 99%

Better subset regression

Xiong¹

2013

Biometrika

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To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance.Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model fitting) than all that do not in a general asymptotic setting. This indicates that, for screening important variables, we could follow a "better fitting, better screening" rule, i.e., pick a "better" subset that has better model fitting. To seek such a better subset, we consider the optimization problem associated with best subset regression. An EM algorithm, called orthogonalizing subset screening, and its accelerating version are proposed for searching for the best subset. Although the two algorithms cannot guarantee that a subset they yield is the best, their monotonicity property makes the subset have better model fitting than initial subsets generated by popular screening methods, and thus the subset can have better screening performance asymptotically. Simulation results show that our methods are very competitive in high dimensional variable screening even for finite sample sizes.

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2D Image Registration in CT Images Using Radial Image Descriptors

Cited by 50 publications

References 6 publications

Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified communication

Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified communication

Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls

Better subset regression

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