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Low-latency Visual Previews of Large Synchrotron Micro-CT Datasets

Tan Jerome, Nicholas ORCID iD icon 1; Chilingaryan, Suren ORCID iD icon 1; van de Kamp, Thomas 2,3; Kopmann, Andreas ORCID iD icon 1
1 Institut für Prozessdatenverarbeitung und Elektronik (IPE), Karlsruher Institut für Technologie (KIT)
2 Laboratorium für Applikationen der Synchrotronstrahlung (LAS), Karlsruher Institut für Technologie (KIT)
3 Institut für Photonenforschung und Synchrotronstrahlung (IPS), Karlsruher Institut für Technologie (KIT)

Abstract:

The unprecedented rate at which synchrotron radia- tion facilities are producing micro-computed (micro-CT) datasets has resulted in an overwhelming amount of data that scientists struggle to browse and interact with in real-time. Thousands of arthropods are scanned into micro-CT within the NOVA project, producing a large collection of gigabyte-sized datasets. In this work, we present methods to reduce the size of this data, scaling it from gigabytes to megabytes, enabling the micro-CT dataset to be delivered in real-time. In addition, arthropods can be identified by scientists even after implementing data reduction methodologies. Our initial step is to devise three distinct visual previews that comply with the best practices of data exploration. Subsequently, each visual preview warrants its own design consideration, thereby necessitating an individual data processing pipeline for each. We aim to present data reduction algorithms applied across the data processing pipelines. Particularly, we reduce size by using the multi-resolution slicemaps, the server- side rendering, and the histogram filtering approaches. In the evaluation, we examine the disparities of each method to identify the most favorable arrangement for our operation, which can then be adjusted for other experiments that have comparable necessities. ... mehr


Volltext §
DOI: 10.5445/IR/1000167712
Veröffentlicht am 30.01.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photonenforschung und Synchrotronstrahlung (IPS)
Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Laboratorium für Applikationen der Synchrotronstrahlung (LAS)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 15.12.2023
Sprache Englisch
Identifikator KITopen-ID: 1000167712
HGF-Programm 54.12.02 (POF IV, LK 01) System Technologies
Weitere HGF-Programme 56.13.11 (POF IV, LK 01) Building Blocks of Life: Structure and Function
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Umfang 6 S.
Projektinformation 05K2016 - NOVA (BMBF, 05K16VKB)
Vorab online veröffentlicht am 25.11.2023
Schlagwörter Computer Vision and Pattern Recognition (cs.CV)
Nachgewiesen in Dimensions
arXiv
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