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Boundary Aware Reconstruction of Scalar Fields
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: 0000-0002-5220-633X
School of Computing, University of Utah, USA.
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
2014 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 20, no 12, 2447-2455 p.Article in journal (Refereed) Published
Abstract [en]

In visualization, the combined role of data reconstruction and its classification plays a crucial role. In this paper we propose a novel approach that improves classification of different materials and their boundaries by combining information from the classifiers at the reconstruction stage. Our approach estimates the targeted materials’ local support before performing multiple material-specific reconstructions that prevent much of the misclassification traditionally associated with transitional regions and transfer function (TF) design. With respect to previously published methods our approach offers a number of improvements and advantages. For one, it does not rely on TFs acting on derivative expressions, therefore it is less sensitive to noisy data and the classification of a single material does not depend on specialized TF widgets or specifying regions in a multidimensional TF. Additionally, improved classification is attained without increasing TF dimensionality, which promotes scalability to multivariate data. These aspects are also key in maintaining low interaction complexity. The results are simple-to-achieve visualizations that better comply with the user’s understanding of discrete features within the studied object.

Place, publisher, year, edition, pages
IEEE Press, 2014. Vol. 20, no 12, 2447-2455 p.
National Category
Computer and Information Science Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-110227DOI: 10.1109/TVCG.2014.2346351ISI: 000344991700090OAI: oai:DiVA.org:liu-110227DiVA: diva2:743626
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
2. Enhancing Salient Features in Volumetric Data Using Illumination and Transfer Functions
Open this publication in new window or tab >>Enhancing Salient Features in Volumetric Data Using Illumination and Transfer Functions
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The visualization of volume data is a fundamental component in the medical domain. Volume data is used in the clinical work-flow to diagnose patients and is therefore of uttermost importance. The amount of data is rapidly increasing as sensors, such as computed tomography scanners, become capable of measuring more details and gathering more data over time. Unfortunately, the increasing amount of data makes it computationally challenging to interactively apply high quality methods to increase shape and depth perception. Furthermore, methods for exploring volume data has mostly been designed for experts, which prohibits novice users from exploring volume data. This thesis aims to address these challenges by introducing efficient methods for enhancing salient features through high quality illumination as well as methods for intuitive volume data exploration.

Humans are interpreting the world around them by observing how light interacts with objects. Shadows enable us to better determine distances while shifts in color enable us to better distinguish objects and identify their shape. These concepts are also applicable to computer generated content. The perception in volume data visualization can therefore be improved by simulating real-world light interaction. This thesis presents efficient methods that are capable of interactively simulating realistic light propagation in volume data. In particular, this work shows how a multi-resolution grid can be used to encode the attenuation of light from all directions using spherical harmonics and thereby enable advanced interactive dynamic light configurations. Two methods are also presented that allow photon mapping calculations to be focused on visually changing areas.The results demonstrate that photon mapping can be used in interactive volume visualization for both static and time-varying volume data.

Efficient and intuitive exploration of volume data requires methods that are easy to use and reflect the objects that were measured. A value that has been collected by a sensor commonly represents the material existing within a small neighborhood around a location. Recreating the original materials is difficult since the value represents a mixture of them. This is referred to as the partial-volume problem. A method is presented that derives knowledge from the user in order to reconstruct the original materials in a way which is more in line with what the user would expect. Sharp boundaries are visualized where the certainty is high while uncertain areas are visualized with fuzzy boundaries. The volume exploration process of mapping data values to optical properties through the transfer function has traditionally been complex and performed by expert users. A study at a science center showed that visitors favor the presented dynamic gallery method compared to the most commonly used transfer function editor.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. 61 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1789
National Category
Media and Communication Technology Computer Science Media Engineering Other Computer and Information Science
Identifiers
urn:nbn:se:liu:diva-131023 (URN)10.3384/diss.diva-131023 (DOI)9789176856895 (ISBN)
Public defence
2016-10-21, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 09:30 (English)
Opponent
Supervisors
Available from: 2016-10-04 Created: 2016-09-05 Last updated: 2016-10-04Bibliographically approved

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Lindholm, StefanJönsson, DanielYnnerman, Anders

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