open_iA is an open source tool for the visual analysis and processing of volumetric datasets, with a focus on industrial CT datasets.

As graphical user interface the cross-plattform framework Qt is used, which facilitates an easy to use and attractive interface. In-house visualisation and image processing algorithms are supported by algorithms of the ITK and VTK toolkit, which make open_iA a powerful tool for both 3D visualisation and CT data analysis. open_iA is capable of loading various volume dataset formats as well as different surface model formats. It provides slice by slice navigation in its 2D views, common 3D navigation with arbitrary cutting planes in the 3D view, together with custom views for individual visualization. open_iA is easily extensible and serves as central development platform of the research group computed tomography and therefore integrates all algorithms and methods developed within the group. And best of it all - it is open source!

Take the opportunity to experience open_iA on github:

Selected Tools:


Anw Software iAnalyse2 B600px

Fiber-reinforced polymers belong to the group of high-performance composite materials and are characterized by their high mechanical strength at simultaneously low weight. Since the fibers in these materials strongly affect the mechanical properties such as stiffness, strength, ductility, etc., it is important to characterize them precisely in order to optimize the material systems.
FeatureScout is designed to accurately analyze fiber-reinforced polymers with respect to their fiber/pore properties (for example, distribution of fiber lengths and fiber orientation). The individual fibers/pores can be selected and displayed by certain criteria. Fibers/pores with similar characteristics can be grouped into classes and stored with additional statistical information in a list. Specific visualization methods simultaneously show the spatial position of all defined fiber/pore classes in the CT volume data set.

Johannes Weissenböck, Artem Amirkhanov, Weimin Li, Andreas Reh, Artem Amirkhanov, Eduard Gröller, Johann Kastner, Christoph Heinzl: "FiberScout: An Interactive Tool for Exploring and Analyzing Fiber Reinforced Polymers", 2014 IEEE Pacific Visualization Symposium, Yokohama, 2014, pp. 153-160. doi:10.1109/PacificVis.2014.52



open iA InSpectr

InSpectr is an integrated tool for the interactive exploration and visual analysis of multimodal,multiscalar data. It addresses the increasing demand in industry for methods to analyze and visualize multimodal data involving a spectral modality. Two data modalities are used: high-resolution X-ray computed tomography (XCT) for structural characterization and low-resolution X-ray fluorescence (XRF) spectral data for elemental decomposition. The tool has been designed around a set of tasks identified by domain experts in the fields of XCT and XRF. It supports registered single scalar and spectral datasets optionally coupled with element maps and reference spectra. InSpectr is instantiating various linked views for the integration of spatial and non-spatial information to provide insight into an industrial component’s structural and material composition: Views with volume renderings of composite and individual 3D element maps visualize global material composition; transfer functions defined directly on the spectral data and overlaid pie-chart glyphs show elemental composition in 2D slice-views; A representative aggregated spectrum and spectra density histograms are introduced to provide a global overview in the spectral view. Spectral magic lenses, spectrum probing and elemental composition probing of points using a pie-chart view and a periodic table view aid the local material composition analysis.

Amirkhanov, A., Fröhler, B., Kastner, J., Gröller, E. and Heinzl, C. (2014), InSpectr: Multi-Modal Exploration, Visualization, and Analysis of Spectral Data. Computer Graphics Forum, 33: 91–100. doi:10.1111/cgf.12365



open iA GEMSe

GEMSe is an interactive tool for exploring and analyzing the parameter space of multi-channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter space and computing the corresponding segmentations. A hierarchically clustered image tree provides an overview of variations in the resulting space of label images. Details are provided through exemplary images from the selected cluster and histograms visualizing the parameters and the derived output in the selected cluster. The correlation between parameters and derived output as well as the effect of parameter changes can be explored through interactive filtering and scatter plots.

Fröhler, B., Möller, T. and Heinzl, C. (2016), GEMSe: Visualization-Guided Exploration of Multi-channel Segmentation Algorithms. Computer Graphics Forum, 35: 191–200. doi:10.1111/cgf.12895




PorosityAnalyzer is a novel tool for a detailed evaluation and visual analysis of pore segmentation pipelines to determine the porosity in fiber-reinforced polymers. The tool consists of two modules: the computation module and the analysis module (see image above). The computation module enables a convenient setup and execution of distributed off-line-computations on industrial 3D X-ray computed tomography datasets. It allows the user to assemble individual segmentation pipelines in the form of single pipeline steps, and to specify the parameter ranges as well as the sampling of the parameter-space of each pipeline segment. The result of a single segmentation run consists of the input parameters, the calculated 3D binary-segmentation mask, the resulting porosity value, and other derived results (e.g., segmentation pipeline runtime). The analysis module presents the data at different levels of detail by drill-down filtering in order to determine accurate and robust segmentation pipelines. Overview visualizations allow to initially compare and evaluate the segmentation pipelines. With a scatter plot matrix (SPLOM), the segmentation pipelines are examined in more detail based on their input and output parameters. Individual segmentation-pipeline runs are selected in the SPLOM and visually examined and compared in 2D slice views and 3D renderings by using aggregated segmentation masks and statistical contour renderings.

 J. Weissenböck, A. Amirkhanov, E. Gröller, J. Kastner and C. Heinzl, "PorosityAnalyzer: Visual analysis and evaluation of segmentation pipelines to determine the porosity in fiber-reinforced polymers," 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), Baltimore, MD, 2016, pp. 101-110. doi: 10.1109/VAST.2016.7883516


Visual Analysis of 4DCT Data


Material engineers use interrupted in situ tensile testing together with X-ray Computed Tomography (CT) to analyze composite materials such as Glass Fiber Reinforced Polymers (GFRPs). During an interrupted in situ tensile test, a workpiece of a composite material is scanned multiple times under various tensile load causing the damage. In GFRPs defects of four types are expected to appear: matrix fractures, fiber/matrix debondings, fiber pull-outs, and fiber fractures. We refer a series of CT scans as 4-dimensional CT (4DCT) data. 4DCT data of GFRPs contains information about defects related to the force applied to the workpiece. We designed and developed a tool that detects and classifies defects for 4DCT data of GFRPs. In addition, the tool includes a bundle of visualization techniques helping the user to analyze composite materials. The Defect Viewer highlights defects with visually encoded type in the context of the original CT image. The Defect Density Maps provide an overview of the defect distributions according to type in 2D and 3D. The Final Fracture Surface estimates the material fracture’s location and displays it as a 3D surface. The 3D Magic Lens enables interactive exploration by combining detailed visualizations in the region of interest with overview visualizations as context.

Amirkhanov, A., Amirkhanov, A., Salaberger, D., Kastner, J., Gröller, M. E. and Heinzl, C. (2016), Visual Analysis of Defects in Glass Fiber Reinforced Polymers for 4DCT Interrupted In situ Tests. Computer Graphics Forum, 35: 201–210. doi:10.1111/cgf.12896


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