WAVE: A 3D Online Previewing Framework for Big Data Archives

Jerome, Nicholas Tan; Chilingaryan, S.; Shkarin, Andrei; Kopmann, A.; Zapf, Michael; Lizin, Alexander; Bergmann, T.

doi:10.5220/0006228101520163

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…The 3D interactive visualization system is part of the raycasting framework [29] that allows users to visualize the reconstructed data from the experiment station. Our visualization approach uses the GPU direct volume rendering approach [13] that performs ray casting on the voxel data.…”

Section: Ct Data and 3d Visualisationmentioning

confidence: 99%

Real-Time Local Noise Filter in 3-D Visualization of CT Data

Jerome

Ateyev

Schmelzle

et al. 2019

IEEE Trans. Nucl. Sci.

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Removing noise in computer tomography (CT) data for real-time 3D visualization is vital to improving the quality of the final display. However, the CT noise cannot be removed by straight averaging because the noise has a broadband spatial frequency that is overlapping with the interesting signal frequencies. To improve the display of structures and features contained in the data, we present spatially variant filtering that performs averaging of sub-regions around a central region. We compare our filter with four other similar spatially variant filters regarding entropy and processing time. The results demonstrate significant improvement of the visual quality with processing time still within the millisecond range.

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Section: Ct Data and 3d Visualisationmentioning

confidence: 99%

Real-Time Local Noise Filter in 3-D Visualization of CT Data

Jerome

Ateyev

Schmelzle

et al. 2019

IEEE Trans. Nucl. Sci.

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“…4). In the preceding ASTOR project, the development of the WAVE web visualization toolkit has been started [52] . We continued this approach and enhanced the WAVE framework to obtain the best image quality for the smallest amount of data transferred to the client.…”

Section: Visualization Services and Computingmentioning

confidence: 99%

The NOVA project: maximizing beam time efficiency through synergistic analyses of SRμCT data

Schmelzle

Heethoff

Heuveline

et al. 2017

Developments in X-Ray Tomography XI

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Beamtime and resulting SRμCT data are a valuable resource for researchers of a broad scientific community in life sciences. Most research groups, however, are only interested in a specific organ and use only a fraction of their data. The rest of the data usually remains untapped. By using a new collaborative approach, the NOVA project (Network for Online Visualization and synergistic Analysis of tomographic data) aims to demonstrate, that more efficient use of the valuable beam time is possible by coordinated research on different organ systems. The biological partners in the project cover different scientific aspects and thus serve as model community for the collaborative approach. As proof of principle, different aspects of insect head morphology will be investigated (e.g., biomechanics of the mouthparts, and neurobiology with the topology of sensory areas). This effort is accomplished by development of advanced analysis tools

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Introducing Biomedisa as an open-source online platform for biomedical image segmentation

et al. 2020

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We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.

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WAVE: A 3D Online Previewing Framework for Big Data Archives

Cited by 6 publications

References 23 publications

Real-Time Local Noise Filter in 3-D Visualization of CT Data

Real-Time Local Noise Filter in 3-D Visualization of CT Data

The NOVA project: maximizing beam time efficiency through synergistic analyses of SRμCT data

Introducing Biomedisa as an open-source online platform for biomedical image segmentation

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