liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Spatial Conditioning of Transfer Functions Using Local Material Distributions
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
Siemens Corporate Research, USA .ORCID iD: 0000-0002-9288-5322
Sectra Imtec AB, Sweden .ORCID iD: 0000-0002-9368-0177
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-9446-6981
Show others and affiliations
2010 (English)In: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, ISSN 1077-2626, Vol. 16, no 6, 1301-1310 p.Article in journal (Refereed) Published
Abstract [en]

In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patients symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the users specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.

Place, publisher, year, edition, pages
IEEE , 2010. Vol. 16, no 6, 1301-1310 p.
Keyword [en]
Direct Volume Rendering, Transfer Function, Spatial Conditioning, Neighborhood Meta-Data
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-62739DOI: 10.1109/TVCG.2010.195ISI: 000283758600050OAI: diva2:374220
Available from: 2010-12-03 Created: 2010-12-03 Last updated: 2015-09-22Bibliographically 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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1614
National Category
Computer and Information Science Computer Science Medical Image Processing
urn:nbn:se:liu:diva-110239 (URN)10.3384/diss.diva-110239 (DOI)978-91-7519-256-7 (print) (ISBN)
Public defence
2014-10-03, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 09:00 (English)
Available from: 2014-09-04 Created: 2014-09-04 Last updated: 2015-09-22Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Lindholm, StefanLjung, PatricLundström, ClaesPersson, AndersYnnerman, Anders
By organisation
Media and Information TechnologyThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)RadiologyFaculty of Health SciencesDepartment of Radiology in Linköping
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
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

Altmetric score

Total: 258 hits
ReferencesLink to record
Permanent link

Direct link