Feature Exploration using Local Frequency Distributions in Computed Tomography Data

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dc.contributor.authorFalk, Martinen_US
dc.contributor.authorLjung, Patricen_US
dc.contributor.authorLundström, Claesen_US
dc.contributor.authorYnnerman, Andersen_US
dc.contributor.authorHotz, Ingriden_US
dc.contributor.editorKozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgiaen_US
dc.date.accessioned2020-09-28T06:11:22Z
dc.date.available2020-09-28T06:11:22Z
dc.date.issued2020
dc.description.abstractFrequency 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.en_US
dc.description.sectionheadersFeature Analysis
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20201166
dc.identifier.isbn978-3-03868-109-0
dc.identifier.issn2070-5786
dc.identifier.pages13-24
dc.identifier.urihttps://doi.org/10.2312/vcbm.20201166
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20201166
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectScientific visualization
dc.subjectVisualization techniques
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.titleFeature Exploration using Local Frequency Distributions in Computed Tomography Dataen_US
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VCBM 2020: Eurographics Workshop on Visual Computing for Biology and Medicine