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

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Towards Clinical Deployment of Automated Anatomical Regions-Of-Interest
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, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0003-0908-9470
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9466-9826
Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
Show others and affiliations
2014 (English)In: Eurographics Workshop on Visual Computing for Biology and Medicine / [ed] Ivan Viola and Katja Buehler and Timo Ropinski, Eurographics - European Association for Computer Graphics, 2014, 137-143 p.Conference paper, Published paper (Refereed)
Abstract [en]

The purpose of this work is to investigate, and improve, the feasibility of advanced Region Of Interest (ROI) selection schemes in clinical volume rendering. In particular, this work implements and evaluates an Automated Anatomical ROI (AA-ROI) approach based on the combination of automatic image registration (AIR) and Distance-Based Transfer Functions (DBTFs), designed for automatic selection of complex anatomical shapes without relying on prohibitive amounts of interaction. Domain knowledge and clinical experience has been included in the project through participation of practicing radiologists in all phases of the project. This has resulted in a set of requirements that are critical for Direct Volume Rendering applications to be utilized in clinical practice and a prototype AA-ROI implementation that was developed to addresses critical points in existing solutions. The feasibility of the developed approach was assessed through a study where five radiologists investigated three medical data sets with complex ROIs, using both traditional tools and the developed prototype software. Our analysis indicate that advanced, registration based ROI schemes could increase clinical efficiency in time-critical settings for cases with complex ROIs, but also that their clinical feasibility is conditional with respect to the radiologists trust in the registration process and its application to the data.

Place, publisher, year, edition, pages
Eurographics - European Association for Computer Graphics, 2014. 137-143 p.
Series
Eurographics Workshop on Visual Computing for Biology and Medicine, ISSN 2070-5778
National Category
Computer and Information Science Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-110233DOI: 10.2312/vcbm.20141199ISBN: 978-3-905674-62-0 (print)OAI: oai:DiVA.org:liu-110233DiVA: diva2:743636
Conference
VCBM 2014 : Eurographics Workshop on Visual Computing for Biology and Medicine, September 4-5, Vienna, Austira
Available from: 2014-09-04 Created: 2014-09-04 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.
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

No full text

Other links

Publisher's full text

Authority records BETA

Lindholm, StefanForsberg, DanielYnnerman, AndersKnutsson, HansAndersson, MatsLundström, Claes

Search in DiVA

By author/editor
Lindholm, StefanForsberg, DanielYnnerman, AndersKnutsson, HansAndersson, MatsLundström, Claes
By organisation
Media and Information TechnologyThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)Department of Biomedical Engineering
Computer and Information ScienceComputer Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 381 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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