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Feature Exploration in Medical Volume Data using Local Frequency Distributions
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Scientific Visualization Group)ORCID iD: 0000-0003-1511-5006
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9288-5322
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9368-0177
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9466-9826
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2020 (English)In: / [ed] B. Kozlíková, M. Krone, and N. N. Smit, 2020Conference paper, Published paper (Refereed)
Abstract [en]

Frequency distributions (FD) are an important instrument when analyzing and investigating scientific data. In volumetric visualization, for example, frequency distributions visualized as histograms, often assist the user in the process of designing transfer function (TF) primitives. Yet a single point in the distribution can correspond to multiple features in the data, particularly in low-dimensional TFs that dominate time-critical domains such as health care. In this paper, we propose contributions to the area of medical volume data exploration, in particular Computed Tomography (CT) data, based on the decomposition of local frequency distributions (LFD). By considering the local neighborhood utilizing LFDs we can incorporate a measure for neighborhood similarity to differentiate features thereby enhancing the classification abilities of existing methods. This also allows us to link the attribute space of the histogram with the spatial properties of the data to improve the user experience and simplify the exploration step. We propose three approaches for data exploration which we illustrate with several visualization cases highlighting distinct features that are not identifiable when considering only the global frequency distribution. We demonstrate the power of the method on selected datasets.

Place, publisher, year, edition, pages
2020.
Keywords [en]
Scientific Visualization, Frequency Distributions, Volume Rendering
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-170755DOI: 10.2312/vcbm.20201166OAI: oai:DiVA.org:liu-170755DiVA, id: diva2:1477832
Conference
Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), Tübingen, Germany, September 28 – October 1, 1010
Funder
Swedish e‐Science Research CenterELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsAvailable from: 2020-10-20 Created: 2020-10-20 Last updated: 2021-09-23Bibliographically approved

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Falk, MartinLjung, PatricLundström, ClaesYnnerman, AndersHotz, Ingrid

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Falk, MartinLjung, PatricLundström, ClaesYnnerman, AndersHotz, Ingrid
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