Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Hybrid Data Visualization Based On Depth Complexity Histogram Analysis
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-2849-6146
Show others and affiliations
2014 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 34, no 1, 74-85 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2014. Vol. 34, no 1, 74-85 p.
National Category
Computer and Information Science Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-110238DOI: 10.1111/cgf.12460ISI: 000350145600008OAI: oai:DiVA.org:liu-110238DiVA: diva2:743638
Note

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

Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Medical Volume Visualization Beyond Single Voxel Values
Open this publication in new window or tab >>Medical Volume Visualization Beyond Single Voxel Values
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 79 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1614
National Category
Computer and Information Science Computer Science Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-110239 (URN)10.3384/diss.diva-110239 (DOI)978-91-7519-256-7 (ISBN)
Public defence
2014-10-03, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 09:00 (English)
Opponent
Supervisors
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2015-09-22Bibliographically approved

Open Access in DiVA

fulltext(3355 kB)213 downloads
File information
File name FULLTEXT01.pdfFile size 3355 kBChecksum SHA-512
d9d7bbbaf44668abd42ae64c79a60fe426f42603fd38355bead503f71a4ecc2fe0bcb3ea1861aa1020e28d275ac3abc6921255d7dd15d84f03089286ba3f7134
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Lindholm, StefanFalk, MartinSundén, ErikBock, AlexanderYnnerman, AndersRopinski, Timo
By organisation
Media and Information TechnologyThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)
In the same journal
Computer graphics forum (Print)
Computer and Information ScienceComputer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 213 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 513 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf