State of the Art in Transfer Functions for Direct Volume Rendering

Multi‐modal data of the complex human anatomy contain a wealth of information. To visualize and explore such data, techniques for emphasizing important structures and controlling visibility are essential. Such fused overview visualizations guide physicians to suspicious regions to be analysed in detail, e.g. with slice‐based viewing. We give an overview of state of the art in multi‐modal medical data visualization techniques. Multi‐modal medical data consist of multiple scans of the same subject using various acquisition methods, often combining multiple complimentary types of information. Three‐dimensional visualization techniques for multi‐modal medical data can be used in diagnosis, treatment planning, doctor–patient communication as well as interdisciplinary communication. Over the years, multiple techniques have been developed in order to cope with the various associated challenges and present the relevant information from multiple sources in an insightful way. We present an overview of these techniques and analyse the specific challenges that arise in multi‐modal data visualization and how recent works aimed to solve these, often using smart visibility techniques. We provide a taxonomy of these multi‐modal visualization applications based on the modalities used and the visualization techniques employed. Additionally, we identify unsolved problems as potential future research directions.

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“…A complete survey on transfer functions for volumetric data can be found in the work by Ljung et al . [LKG*16].…”

Section: Rendering and Interaction Techniques For Multi‐modal Data VImentioning

confidence: 99%

A Survey on Multimodal Medical Data Visualization

Lawonn

Smit

Bühler

et al. 2017

Computer Graphics Forum

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“…Transfer functions (TFs) are mainly applied in (interactive) volume rendering and define a direct mapping of scalar values into visual attributes like color and opacity [Kin02]. The specification of transfer functions is essentially a classification problem [EHK*06]; see Ljung et al [LKG*16] for a recent survey on direct volume rendering. In the context of multi‐variate interactive visual analysis, MD‐TFs [KKH02] can be used as an alternative method for interactive feature specification .…”

Section: Multi‐variate Data Analysis Techniquesmentioning

confidence: 99%

Visual Analysis of Confocal Raman Spectroscopy Data using Cascaded Transfer Function Design

Schikora

Plack

Bornemann

et al. 2017

Computer Graphics Forum

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2D Confocal Raman Microscopy (CRM) data consist of high dimensional per‐pixel spectral data of 1000 bands and allows for complex spectral and spatial‐spectral analysis tasks, i.e., in material discrimination, material thickness, and spatial material distributions. Currently, simple integral methods are commonly applied as visual analysis solutions to CRM data which exhibit restricted discrimination power in various regards. In this paper we present a novel approach for the visual analysis of 2D multispectral CRM data using multi‐variate visualization techniques. Due to the large amount of data and the demand of an explorative approach without a‐priori restriction, our system allows for arbitrary interactive (de)selection of varaibles w/o limitation and an unrestricted online definition/construction of new, combined properties. Our approach integrates CRM specific quantitative measures and handles material‐related features for mixed materials in a quantitative manner. Technically, we realize the online definition/construction of new, combined properties as semi‐automatic, cascaded, 1D and 2D multidimensional transfer functions (MD‐TFs). By interactively incorporating new (raw or derived) properties, the dimensionality of the MD‐TF space grows during the exploration procedure and is virtually unlimited. The final visualization is achieved by an enhanced color mixing step which improves saturation and contrast.

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“…In this process, each voxel would 96 be classified as either WM, GM, or CSF by specifying appropriate transfer functions. It 97 has been shown, however, that this approach is less successful than established brain 98 segmentation algorithms [45]. Here, we propose that transfer function-based methods specifying effective, 2D transfer functions [36,38].…”

mentioning

confidence: 95%

“…This currently leaves researchers with the dilemma of accepting the likely 41 erroneous outcome of automatic segmentation algorithms or performing a 42 time-consuming and error-prone manual correction. 43 CBS tools [32] directly tackle many of the challenges of UHF high-resolution 44 anatomical data by, for example, including pre-processing steps to estimate dura mater 45 and CSF partial voluming. Consequently, these tools provide an improved initial GM problems of an entirely manual correction, since it yields a meaningful summary 62 representation of the data that allows to manipulate the data efficiently.…”

mentioning

confidence: 99%

A scalable method to improve gray matter segmentation at ultra high field MRI

Gulban

Schneider

Marquardt

et al. 2018

Preprint

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High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

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State of the Art in Transfer Functions for Direct Volume Rendering

Cited by 103 publications

References 141 publications

A Survey on Multimodal Medical Data Visualization

A Survey on Multimodal Medical Data Visualization

Visual Analysis of Confocal Raman Spectroscopy Data using Cascaded Transfer Function Design

A scalable method to improve gray matter segmentation at ultra high field MRI

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