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Hybrid Data Visualization Based On Depth Complexity Histogram Analysis
Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-2849-6146
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2014 (Engelska)Ingår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 34, nr 1, s. 74-85Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi-transparent geometry is present, correct rendering results require sorting of transparent structures. Additional complexity is introduced as the contributions from volumetric data have to be partitioned according to the geometric objects in the scene. The A-buffer, an enhanced framebuffer with additional per-pixel information, has previously been introduced to deal with the complexity caused by transparent objects. In this paper, we present an optimized rendering algorithm for hybrid volume-geometry data based on the A-buffer concept. We propose two novel components for modern GPUs that tailor memory utilization to the depth complexity of individual pixels. The proposed components are compatible with modern A-buffer implementations and yield performance gains of up to eight times compared to existing approaches through reduced allocation and reuse of fast cache memory. We demonstrate the applicability of our approach and its performance with several examples from molecular biology, space weather, and medical visualization containing both, volumetric data and geometric structures.

Ort, förlag, år, upplaga, sidor
John Wiley & Sons, 2014. Vol. 34, nr 1, s. 74-85
Nationell ämneskategori
Data- och informationsvetenskap Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:liu:diva-110238DOI: 10.1111/cgf.12460ISI: 000350145600008OAI: oai:DiVA.org:liu-110238DiVA, id: diva2:743638
Anmärkning

On the day of the defence date the status of this publication was Manuscript.

Tillgänglig från: 2014-09-04 Skapad: 2014-09-04 Senast uppdaterad: 2018-01-11Bibliografiskt granskad
Ingår i avhandling
1. Medical Volume Visualization Beyond Single Voxel Values
Öppna denna publikation i ny flik eller fönster >>Medical Volume Visualization Beyond Single Voxel Values
2014 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Medical visualization involves many complex decisions for both the user and the imaging algorithms. This thesis aims to improve medical volume visualization through a series of technical contributions to aid such decision processes. Improvements are achieved by using more data, beyond single voxels, in the associated visual analyses.

Simultaneous visualization of multiple data sources and different data formats is rapidly becoming a necessity. This is due to both the growing number of data producing image acquisition techniques as well as the increase in geometric data representations that can be created. Maintaining high rendering performance under these circumstances is challenging, but necessary, to support an exploratory visualization process. This thesis proposes two algorithms to address this challenge: a multi-volume approach that applies binary-space partitioning to solve painters' algorithm geometrically and a rendering algorithm for hybrid data that improves the management of the available graphics memory.

Additional information for decision support is often derived from the captured image data. Classification techniques, in particular, often utilize secondary information sources or neighborhood analysis as means to improve specificity. One example is a proposed algorithm that improves visualization of blood vessels by automatically optimizing visualization parameters based on observed vesselness. This thesis also proposes algorithms involving neighborhood analysis, with a particular focus on domain specific classification knowledge provided by the user. One algorithm provides the ability to semantically state spatial relations between tissues based on encoded material information. Another algorithm improves the representation of discrete features by integrating the users' knowledge in the reconstruction step of the visualization pipeline.

Many of the methods proposed in this thesis can also be applied to other domains, but are all described here in the context of medical volume visualization as most of the research has been performed within this field.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2014. s. 79
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1614
Nationell ämneskategori
Data- och informationsvetenskap Datavetenskap (datalogi) Medicinsk bildbehandling
Identifikatorer
urn:nbn:se:liu:diva-110239 (URN)10.3384/diss.diva-110239 (DOI)978-91-7519-256-7 (ISBN)
Disputation
2014-10-03, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 09:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2014-09-04 Skapad: 2014-09-04 Senast uppdaterad: 2018-01-11Bibliografiskt granskad

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Lindholm, StefanFalk, MartinSundén, ErikBock, AlexanderYnnerman, AndersRopinski, Timo

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