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@article{perez2025OpenTwinGridConstructing,
author = {Perez, Ernesto and Floros, Dimitris and Norris, Tyler H and {Patino-Echeverri}, Dalia},
langid = {english},
note = {Under review},
year = {2025},
journal = {IEEE Transactions on Power Systems},
title = {{{OpenTwinGrid}}: {{Constructing Realistic Power Transmission Network Models}} from {{Public Sources}}},
abstract = {This paper introduces the OpenTwinGrid protocol, a systematic and scalable framework for constructing geographically realistic and topologically accurate transmission network models for any U.S. balancing authority using publicly available data. Access to detailed transmission network models is often restricted due to confidentiality and security constraints, creating major barriers to transparent and reproducible research in power system studies. The protocol addresses critical gaps in existing open-access grid modeling, which often lacks the fidelity and physical realism needed for accurate power system analysis. OpenTwinGrid provides robust procedures for estimating transmission line parameters---including impedances and MVA ratings---inferring network topology, and geo-locating and characterizing generation assets from public datasets. The resulting network models reflect the topological structure and operational characteristics of real-world grids. We illustrate the approach with a case study for the Duke Energy Carolinas and Duke Energy Progress balancing authorities and validate it. The OpenTwinGrid models support a wide range of power system applications, including renewable integration studies, interconnection assessments, large-load siting, and resilience analysis.}
}
This paper introduces the OpenTwinGrid protocol, a systematic and scalable framework for constructing geographically realistic and topologically accurate transmission network models for any U.S. balancing authority using publicly available data. Access to detailed transmission network models is often restricted due to confidentiality and security constraints, creating major barriers to transparent and reproducible research in power system studies. The protocol addresses critical gaps in existing open-access grid modeling, which often lacks the fidelity and physical realism needed for accurate power system analysis. OpenTwinGrid provides robust procedures for estimating transmission line parameters---including impedances and MVA ratings---inferring network topology, and geo-locating and characterizing generation assets from public datasets. The resulting network models reflect the topological structure and operational characteristics of real-world grids. We illustrate the approach with a case study for the Duke Energy Carolinas and Duke Energy Progress balancing authorities and validate it. The OpenTwinGrid models support a wide range of power system applications, including renewable integration studies, interconnection assessments, large-load siting, and resilience analysis.
@unpublished{zotero-9667,
keywords = {df-eprint},
author = {Floros, Dimitris and Zhang, Xiaodong and {Patino-Echeverri}, Dalia},
year = {2025},
title = {Cost, Reliability, and Environmental Benefits of a Risk-Adjusted Stochastic Unit Commitment Model for Systems with Large Long-Duration Energy Storage Assets}
}
@incollection{floros2024FlexibleStorageb,
keywords = {df-presentation},
booktitle = {Proceedings of the 46th {{IAEE}} International Conference},
author = {Floros, Dimitris and Norris, Tyler and Gonzalez, Ernesto Perez and {Patino-Echeverri}, Dalia},
langid = {english},
series = {{{IAEE}} Conference Proceedings},
year = {2025},
title = {Synthesizing {{Realistic Electric Power Transmission Networks}} for {{Expediting Interconnection Studies}}}
}
@unpublished{wang2024clusteringneural,
keywords = {df-article},
author = {Wang, Wei and Floros, Dimitris and Bhattacharya, Arnab and Sharma, Himanshu and Adetola, Veronica and {Patino-Echeverri}, Dalia},
note = {Under review},
year = {2024},
title = {A Clustering and Neural Network Based Learning Approach to Generate Probabilistic Scenarios for Stochastic Unit Commitment}
}
@unpublished{hernandez2024learningsolve,
keywords = {df-article},
author = {Hernandez, Mauricio and Floros, Dimitris and Bradbury, Kyle and {Patino-Echeverri}, Dalia},
note = {Under review},
year = {2024},
title = {Learning to Solve the Unit Commitment Problem}
}
@inproceedings{floros2024electricpower,
keywords = {df-presentation},
address = {Grapevine, TX, USA},
booktitle = {{{ARPA-E Energy Innovation Summit}}},
author = {Floros, Dimitris and Liu, Xuan and {Patino-Echeverri}, Dalia},
year = {2024},
title = {{{GRACE}}: A Grid That Is Risk-Aware for Clean Electricity}
}
@incollection{floros2024flexiblestoragea,
keywords = {df-presentation},
booktitle = {Proceedings of the 45th {{IAEE}} International Conference},
author = {Floros, Dimitris and Zhang, Xiaodong and Hernandez, Mauricio and {Patino-Echeverri}, Dalia},
langid = {english},
series = {{{IAEE}} Conference Proceedings},
year = {2024},
title = {Flexible {{Storage Commitment}} in {{Energy Management Systems}} under {{Uncertainty}}}
}
@incollection{floros2024generatingprobabilistic,
keywords = {df-presentation},
booktitle = {Proceedings of the 45th {{IAEE}} International Conference},
author = {Floros, Dimitris and Wang, Wei and Hernandez, Mauricio and Kern, Jordan and {Patino-Echeverri}, Dalia},
langid = {english},
series = {{{IAEE}} Conference Proceedings},
year = {2024},
title = {Generating {{Probabilistic Scenario Ensembles}} for {{Stochastic Unit Commitment}}}
}
@inproceedings{floros2024AlgebraicVertex,
pages = {1--7},
booktitle = {{{IEEE High Performance Extreme Computing Conference}}},
author = {Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
title = {Algebraic {{Vertex Ordering}} of a {{Sparse Graph}} for {{Adjacency Access Locality}} and {{Graph Compression}}},
doi = {10.1109/HPEC62836.2024.10938496},
keywords = {df-conference},
copyright = {https://doi.org/10.15223/policy-029},
urldate = {2025-04-16},
year = {2024}
}
@inproceedings{floros2024gracegrid,
keywords = {df-presentation},
address = {Pittsburgh, PA, USA},
booktitle = {{{CMU Doctoral Student Participatory Workshop}} on {{Climate}} and {{Energy Decision Making}}},
author = {Floros, Dimitris and {Patino-Echeverri}, Dalia},
year = {2024},
title = {Probabilistic {{Forecast Generator}} to {{Enhance Uncertainty Characterization}} in {{Stochastic Unit Commitment}}}
}
@inproceedings{floros2024improving,
keywords = {df-presentation},
address = {United States},
booktitle = {{{INFORMS}} Annual Meeting 2024},
author = {Floros, Dimitrios and Hernandez, Mauricio and Bradbury, Kyle and {Patino-Echeverri}, Dalia},
year = {2024},
month = {October},
title = {Improving the Performance of Risk-Adjusted Stochastic Unit Commitment for Clean Electricity}
}
@inproceedings{patino-echeverri2024grace,
keywords = {df-presentation},
address = {United States},
booktitle = {{{INFORMS}} Annual Meeting 2024},
author = {{Patino-Echeverri}, Dalia and Floros, Dimitrios and Wang, Wei and Hernandez, Mauricio and Kern, Jordan and Zhang, Xiaodong},
year = {2024},
month = {October},
title = {Grace Foreseer: A Probabilistic Forecast Generator for Stochastic Unit Commitment}
}
@incollection{floros2024flexiblestorage,
keywords = {df-presentation},
booktitle = {Proceedings of the {{USAEE}}/{{IAEE North American Conference}}},
author = {Zhang, Xiaodong and Floros, Dimitris and Hernandez, Mauricio and {Patino-Echeverri}, Dalia},
langid = {english},
series = {{{USAEE}}/{{IAEE North American Conference Proceedings}}},
year = {2023},
title = {A Risk-Adjusted Stochastic Unit Commitment Model to Face Increased Uncertainty and Variability from Extreme Weather and Deeper Renewables Penetration}
}
@inproceedings{pitsianis2023parallelclustering,
pages = {1--8},
doi = {10.1109/HPEC58863.2023.10363552},
keywords = {df-conference},
booktitle = {High {{Performance Extreme Computing Conference}}},
author = {Pitsianis, Nikos and Floros, Dimitris and Liu, Tiancheng and Sun, Xiaobai},
urldate = {2024-02-09},
year = {2023},
title = {Parallel {{Clustering}} with {{Resolution Variation}}}
}
@inproceedings{floros2023Electricpower,
keywords = {df-presentation},
address = {National Harbor, MD, USA},
booktitle = {{{ARPA-E Energy Innovation Summit}}},
author = {Floros, Dimitris and Hernandez, Mauricio and Zhang, Xiaodong and {Patino-Echeverri}, Dalia},
year = {2023},
title = {Electric Power System Costs Savings from a Risk-Adjusted Stochastic Unit Commitment Model}
}
@inproceedings{floros2023gracegrid,
keywords = {df-presentation},
address = {Denver, CO, USA},
booktitle = {{{ESIG Meteorology}} and {{Market Design}} for {{Grid Services Workshop}}},
author = {Floros, Dimitris and {Patino-Echeverri}, Dalia},
year = {2023},
title = {{{GRACE}}: A Grid That Is Risk-Aware for Clean Electricity}
}
@unpublished{liuSteerableCommunityDetection2022,
keywords = {df-manuscript},
author = {Liu, Tiancheng and Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
year = {2022},
title = {Steerable {{Community Detection}}}
}
@unpublished{florosFasterMethodBoolean2022,
keywords = {df-manuscript},
author = {Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
year = {2022},
title = {A Faster Method for {{Boolean}} Matrix Multiplication and Triangle Locations on a Network}
}
@inproceedings{floros*2022fastgraph,
pages = {1--8},
booktitle = {{{IEEE High Performance Extreme Computing}}},
author = {Floros*, Dimitris and Liu*, Tiancheng and Pitsianis, Nikos and Sun, Xiaobai},
annotation = {0 citations (Semantic Scholar/DOI) [2023-07-26]},
title = {Fast Graph Algorithms for Superpixel Segmentation},
doi = {10.1109/HPEC55821.2022.9926359},
keywords = {df-conference},
urldate = {2023-07-26},
year = {2022}
}
@article{chatzakisSTORKCollaborativeOnline2022,
pages = {653},
shorttitle = {{{STORK}}},
author = {Chatzakis, Christos and Floros, Dimitris and Liberis, Anastasios and Gerede, Aggeliki and Dinas, Konstantinos and Pitsianis, Nikos and Sotiriadis, Alexandros},
annotation = {0 citations (Semantic Scholar/DOI) [2023-07-26]},
month = {March},
journal = {Healthcare},
title = {{{STORK}}: {{Collaborative Online Monitoring}} of {{Pregnancies Complicated}} with {{Gestational Diabetes Mellitus}}},
number = {4},
doi = {10.3390/healthcare10040653},
keywords = {df-article},
langid = {english},
issn = {2227-9032},
urldate = {2023-07-26},
year = {2022},
volume = {10},
file = {/Users/fcdimitr/Zotero/storage/7WC9TVJ2/Chatzakis et al. - 2022 - STORK Collaborative Online Monitoring of Pregnanc.pdf},
abstract = {Background: A novel digital platform, named STORK, was developed in the COVID-19 pandemic when clinic visits were restricted. A study of its clinical use during the pandemic was conducted. The study aims to advance the state of the art in monitoring and care of pregnancies complicated with gestational diabetes mellitus (GDM) via online collaboration between patients and care providers. Methods: This study involved 31 pregnant women diagnosed with GDM and 5 physicians. Statistical comparisons were made in clinic-visit frequency and adverse outcomes between the STORK group and a historical control group of 32 women, compatible in size, demographics, anthropometrics and medical history. Results: The average number of submitted patient measurements per day was 3.6{\textpm}0.4. The average number of clinic visits was 2.9{\textpm}0.7 for the STORK group vs. 4.1{\textpm}1.1 for the control group (p{$<$}0.05). The number of neonatal macrosomia cases was 2 for the STORK group vs. 3 for the control group (p{$>$}0.05); no other adverse incidents. Conclusions: The patient compliance with the pilot use of STORK was high and the average number of prenatal visits was reduced. The results suggest the general feasibility to reduce the average number of clinic visits and cost, with enhanced monitoring, case-specific adaptation, assessment and care management via timely online collaboration.}
}
Background: A novel digital platform, named STORK, was developed in the COVID-19 pandemic when clinic visits were restricted. A study of its clinical use during the pandemic was conducted. The study aims to advance the state of the art in monitoring and care of pregnancies complicated with gestational diabetes mellitus (GDM) via online collaboration between patients and care providers. Methods: This study involved 31 pregnant women diagnosed with GDM and 5 physicians. Statistical comparisons were made in clinic-visit frequency and adverse outcomes between the STORK group and a historical control group of 32 women, compatible in size, demographics, anthropometrics and medical history. Results: The average number of submitted patient measurements per day was 3.6{\textpm}0.4. The average number of clinic visits was 2.9{\textpm}0.7 for the STORK group vs. 4.1{\textpm}1.1 for the control group (p{$<$}0.05). The number of neonatal macrosomia cases was 2 for the STORK group vs. 3 for the control group (p{$>$}0.05); no other adverse incidents. Conclusions: The patient compliance with the pilot use of STORK was high and the average number of prenatal visits was reduced. The results suggest the general feasibility to reduce the average number of clinic visits and cost, with enhanced monitoring, case-specific adaptation, assessment and care management via timely online collaboration.
@inproceedings{liu2021c,
pages = {1--7},
booktitle = {{{IEEE High Performance Extreme Computing}}},
author = {Liu*, Tiancheng and Floros*, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
isbn = {978-1-6654-2369-4},
annotation = {1 citations (Semantic Scholar/DOI) [2023-07-26]},
title = {Digraph Clustering by the {{BlueRed}} Method},
doi = {10.1109/HPEC49654.2021.9622834},
keywords = {df-conference},
urldate = {2022-07-15},
year = {2021}
}
@inproceedings{floros2021public,
keywords = {df-public-talk},
booktitle = {{{Webinar "Machine Learning and COVID-19" at the Student Branch of IEEE-EMBs at AUTh}}},
author = {Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
year = {2021},
title = {{{LG-covid19-HOTP}}: {{Literature}} Graph of Scholarly Articles Relevant to {{COVID-19}} Study}
}
@inproceedings{florosSystematicAssociationSubgraph2021,
keywords = {Computer Science - Discrete Mathematics,df-eprint},
author = {Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
urldate = {2021-04-22},
year = {2021},
annotation = {Under review},
file = {/Users/fcdimitr/Zotero/storage/CHD9P9LK/Floros_et_al_2021_A_systematic_association_of_subgraph_counts_over_a_network.pdf;/Users/fcdimitr/Zotero/storage/LAQ6FSMB/2103.html},
abstract = {We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as a unified foundation for studying and connecting many important graph problems in theory and practice. We describe topological relations among graphlets (graph elements) in rigorous mathematics language and from the perspective of graph encoding. We uncover, characterize and utilize algebraic and numerical relations in graphlet counts/frequencies. We present a novel algorithm for efficiently counting small subgraphs as a practical product of our theoretical findings.},
title = {A Systematic Association of Subgraph Counts over a Network}
}
We associate all small subgraph counting problems with a systematic graph encoding/representation system which makes a coherent use of graphlet structures. The system can serve as a unified foundation for studying and connecting many important graph problems in theory and practice. We describe topological relations among graphlets (graph elements) in rigorous mathematics language and from the perspective of graph encoding. We uncover, characterize and utilize algebraic and numerical relations in graphlet counts/frequencies. We present a novel algorithm for efficiently counting small subgraphs as a practical product of our theoretical findings.
@inproceedings{chatzakisRemoteMonitoringPregnancies2021,
publisher = {Elsevier},
doi = {10.1016/j.metabol.2020.154592},
keywords = {df-presentation},
booktitle = {Metabolism - {{Clinical}} and {{Experimental}}},
author = {Chatzakis, Christos and Floros, Dimitris and Pitsianis, Nikolaos and Sotiriadis, Alexandros},
urldate = {2021-10-28},
year = {2021},
volume = {116},
title = {Remote {{Monitoring}} of {{Pregnancies Complicated}} by {{Gestational Diabetes Mellitus}} during the {{COVID-19}} : {{Lockdown Using STORK}}}
}
@inproceedings{florosUsingGraphletSpectrograms2020,
pages = {1--7},
doi = {10.1109/HPEC43674.2020.9286161},
keywords = {df-conference},
booktitle = {{{IEEE High Performance Extreme Computing Conference}}},
author = {Floros, Dimitris and Liu, Tiancheng and Pitsianis, Nikos and Sun, Xiaobai},
year = {2020},
title = {Using Graphlet Spectrograms for Temporal Pattern Analysis of Virus-Research Collaboration Networks}
}
@inproceedings{floros2020public,
keywords = {df-public-talk},
booktitle = {{{livemedia.gr}} by {{H}}. {{V}}. {{Bliatka}}},
author = {Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
year = {2020},
month = {May},
title = {{{LG-covid19-HOTP}}: {{Literature}} Graph of Scholarly Articles Relevant to {{COVID-19}} Study}
}
@inproceedings{florosFastGraphletTransform2020,
pages = {1--8},
doi = {10.1109/HPEC43674.2020.9286205},
keywords = {df-conference},
booktitle = {{{IEEE High Performance Extreme Computing Conference}}},
author = {Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai},
year = {2020},
title = {Fast Graphlet Transform of Sparse Graphs}
}
@article{pitsianis2019a,
pages = {1577},
author = {Pitsianis, Nikos and Floros, Dimitris and Iliopoulos, Alexandros-Stavros and Sun, Xiaobai},
journal = {Journal of Open Source Software},
title = {{{SG-t-SNE-$\Pi$}}: Swift Neighbor Embedding of Sparse Stochastic Graphs},
number = {39},
doi = {10.21105/joss.01577},
keywords = {df-code,Valid},
issn = {2475-9066},
year = {2019},
volume = {4}
}
@inproceedings{pitsianisSpacelandEmbeddingSparse2019,
doi = {10.1109/HPEC.2019.8916505},
keywords = {df-conference},
booktitle = {{{IEEE High Performance Extreme Computing Conference}}},
author = {Pitsianis, Nikos and Iliopoulos, Alexandros-Stavros and Floros, Dimitris and Sun, Xiaobai},
year = {2019},
title = {Spaceland Embedding of Sparse Stochastic Graphs}
}
@inproceedings{blanningParametricVariationMoodle2019,
keywords = {df-presentation},
address = {Thessaloniki, Greece},
booktitle = {{{MoodleMoot}}},
author = {Blanning, Frank and Floros, Dimitris and Pitsianis, Nikos},
year = {2019},
title = {Parametric Variation of a Moodle Quiz}
}
@inproceedings{floros2019d,
keywords = {df-presentation},
address = {Gottingen, Germany},
booktitle = {{{Workshop on Data Locality (COLOC), Euro-Par 2019}}},
author = {Floros, Dimitris and Iliopoulos, Alexandros-Stavros and Pitsianis, Nikos and Sun, Xiaobai},
year = {2019},
title = {Multi-Level Data Translocation for Faster Processing of Scattered Data on Shared-Memory Computers}
}
@article{chatzakisBeneficialEffectMobile2019,
pages = {627--634},
shorttitle = {The {{Beneficial Effect}} of the {{Mobile Application}} {{{\emph{Euglyca}}}} in {{Children}} and {{Adolescents}} with {{Type}} 1 {{Diabetes Mellitus}}},
author = {Chatzakis, Christos and Floros, Dimitrios and Papagianni, Maria and Tsiroukidou, Kyriaki and Kosta, Konstantina and Vamvakis, Anastasios and Koletsos, Nikolaos and Hatziagorou, Elpida and Tsanakas, Ioannis and Mastorakos, George},
month = {November},
journal = {Diabetes Technology \& Therapeutics},
title = {The {{Beneficial Effect}} of the {{Mobile Application}} {{{\emph{Euglyca}}}} in {{Children}} and {{Adolescents}} with {{Type}} 1 {{Diabetes Mellitus}}: {{A Randomized Controlled Trial}}},
number = {11},
doi = {10.1089/dia.2019.0170},
keywords = {df-article},
langid = {english},
issn = {1520-9156, 1557-8593},
urldate = {2021-11-05},
year = {2019},
volume = {21}
}
@inproceedings{floros*SparseDualDensity2018,
doi = {10.1109/HPEC.2018.8547519},
keywords = {df-conference},
booktitle = {{{IEEE High Performance Extreme Computing Conference}}},
author = {Floros*, Dimitris and Liu*, Tiancheng and Pitsianis, Nikos and Sun, Xiaobai},
year = {2018},
annotation = {*Co-first authors},
title = {Sparse Dual of the Density Peaks Algorithm for Cluster Analysis of High-Dimensional Data}
}
@article{liuRobustAutomaticCosegmentation2017,
pages = {3024--3025},
keywords = {df-presentation},
author = {Liu, Tiancheng and Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai and Yin, Fang-Fang and Ren, Lei},
year = {2017},
volume = {44},
journal = {Medical physics},
title = {Robust Automatic Co-Segmentation of Multiple Medical Images}
}
@article{pitsianisRapidNearneighborInteraction2017,
primaryClass = {cs.LG},
author = {Pitsianis, Nikos and Floros, Dimitris and Iliopoulos, Alexandros-Stavros and Mylonakis, Kostas and Sismanis, Nikos and Sun, Xiaobai},
journal = {1709.03671 [cs.LG]},
archivePrefix = {arXiv},
title = {Rapid Near-Neighbor Interaction of High-Dimensional Data via Hierarchical Clustering},
keywords = {Computer Science - Machine Learning,df-eprint},
urldate = {2020-07-31},
eprint = {1709.03671},
year = {2017},
abstract = {Calculation of near-neighbor interactions among high dimensional, irregularly distributed data points is a fundamental task to many graph-based or kernel-based machine learning algorithms and applications. Such calculations, involving large, sparse interaction matrices, expose the limitation of conventional data-and-computation reordering techniques for improving space and time locality on modern computer memory hierarchies. We introduce a novel method for obtaining a matrix permutation that renders a desirable sparsity profile. The method is distinguished by the guiding principle to obtain a profile that is block-sparse with dense blocks. Our profile model and measure capture the essential properties affecting space and time locality, and permit variation in sparsity profile without imposing a restriction to a fixed pattern. The second distinction lies in an efficient algorithm for obtaining a desirable profile, via exploring and exploiting multi-scale cluster structure hidden in but intrinsic to the data. The algorithm accomplishes its task with key components for lower-dimensional embedding with data-specific principal feature axes, hierarchical data clustering, multi-level matrix compression storage, and multi-level interaction computations. We provide experimental results from case studies with two important data analysis algorithms. The resulting performance is remarkably comparable to the BLAS performance for the best-case interaction governed by a regularly banded matrix with the same sparsity.}
}
Calculation of near-neighbor interactions among high dimensional, irregularly distributed data points is a fundamental task to many graph-based or kernel-based machine learning algorithms and applications. Such calculations, involving large, sparse interaction matrices, expose the limitation of conventional data-and-computation reordering techniques for improving space and time locality on modern computer memory hierarchies. We introduce a novel method for obtaining a matrix permutation that renders a desirable sparsity profile. The method is distinguished by the guiding principle to obtain a profile that is block-sparse with dense blocks. Our profile model and measure capture the essential properties affecting space and time locality, and permit variation in sparsity profile without imposing a restriction to a fixed pattern. The second distinction lies in an efficient algorithm for obtaining a desirable profile, via exploring and exploiting multi-scale cluster structure hidden in but intrinsic to the data. The algorithm accomplishes its task with key components for lower-dimensional embedding with data-specific principal feature axes, hierarchical data clustering, multi-level matrix compression storage, and multi-level interaction computations. We provide experimental results from case studies with two important data analysis algorithms. The resulting performance is remarkably comparable to the BLAS performance for the best-case interaction governed by a regularly banded matrix with the same sparsity.
@article{iliopoulosAdaptiveDenoisingMultiple2017,
author = {Iliopoulos, Alexandros-Stavros and Floros, Dimitris and Zhang, Y and Pitsianis, Nikos and Sun, Xiaobai and Yin, Fang-Fang and Ren, Lei},
journal = {Medical Physics},
title = {Adaptive Denoising over Multiple Anatomical Regions with Edge and Texture Preservation},
publisher = {John Wiley \& Sons, Ltd},
number = {6},
doi = {10.1118/1.4955838},
keywords = {df-presentation,Image analysis,Medical image noise,Multiscale methods,Parallel processing,Spatial analysis,Spatial filtering,Spatial scaling,Statistical analysis,X-ray effects,X-ray scattering},
urldate = {2020-07-31},
year = {2017},
volume = {44},
abstract = {Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173}
}
Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173
@inproceedings{bontomitsidisLARKLocationAwarePersonalized2016,
keywords = {df-conference},
booktitle = {{{EUNIS}}: {{Crossroads}} Where the Past Meets the Future},
author = {Bontomitsidis, Spiros and Floros, Dimitris and Manolas, Dimitris and Mylonakis, Konstantinos and Pitsianis, Nikos},
year = {2016},
title = {{{LARK}}: {{Location-Aware Personalized Travel Guide}} with {{Rich Knowledge}}}
}
@inproceedings{florosWindowedAllkNNSearch2016,
keywords = {df-presentation},
address = {San Jose, CA, USA},
booktitle = {{{GPU Technology Conference}}},
author = {Floros, Dimitris and Iliopoulos, Alexandros-Stavros and Pitsianis, Nikos and Sun, Xiaobai},
year = {2016},
title = {Windowed All-{{kNN}} Search over Multidimensional Array Data from Medical Imaging}
}
@article{iliopoulosSpatiallyLocalStatistics2016,
author = {Iliopoulos, Alexandros-Stavros and Floros, Dimitris and Zhang, Y and Pitsianis, Nikos and Sun, Xiaobai and Yin, Fang-Fang and Ren, Lei},
journal = {Medical Physics},
title = {Spatially {{Local Statistics}} for {{Adaptive Image Filtering}}},
publisher = {John Wiley \& Sons, Ltd},
number = {6},
doi = {10.1118/1.4955838},
keywords = {df-presentation,Image analysis,Medical image noise,Multiscale methods,Parallel processing,Spatial analysis,Spatial filtering,Spatial scaling,Statistical analysis,X-ray effects,X-ray scattering},
urldate = {2020-07-31},
year = {2016},
volume = {43},
abstract = {Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173}
}
Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173
@inproceedings{iliopoulosLocalStatisticalFiltering2016,
keywords = {df-presentation},
address = {San Jose, CA, USA},
booktitle = {{{GPU Technology Conference}}},
author = {Iliopoulos, Alexandros-Stavros and Floros, Dimitris and Pitsianis, Nikos and Sun, Xiaobai and Yin, Fang-Fang and Ren, Lei},
year = {2016},
title = {Local Statistical Filtering via Domain Dissection for Medical Imaging}
}
@unpublished{hernandezSolutionslearning,
author = {Hernandez, Mauricio and Floros, Dimitris and Bradbury, Kyle and {Patino-Echeverri}, Dalia},
title = {Solutions Learning Algorithm for the Stochastic Unit Commitment Problem}
}