Beamtime and resulting SR mu CT 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 frac...
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ISBN:
(纸本)9781510612402;9781510612396
Beamtime and resulting SR mu CT 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 for the ever-increasing quantity of tomographic datasets. In the preceding project ASTOR, we already successfully demonstrated considerable progress in semi-automatic segmentation and classification of internal structures. Further improvement of these methods is essential for an efficient use of beam time and will be refined in the current NOVA-project. Significant enhancements are also planned at PETRA III beamline p05 to provide all possible contrast modalities in x-ray imaging optimized to biological samples, on the reconstruction algorithms, and the tools for subsequent analyses and management of the data. All improvements made on key technologies within this project will in the long-term be equally beneficial for all users of tomography instrumentations.
With data sets growing beyond terabytes or even petabytes in scientific experiments, there is a trend of keeping data at storage facilities and providing remote cloud-based services for analysis. However, accessing th...
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ISBN:
(纸本)9789897582288
With data sets growing beyond terabytes or even petabytes in scientific experiments, there is a trend of keeping data at storage facilities and providing remote cloud-based services for analysis. However, accessing these data sets remotely is cumbersome due to additional network latency and incomplete metadata description. To ease databrowsing on remote data archives, our WAVE framework applies an intelligent cache management to provide scientists with a visual feedback on the large data set interactively. In this paper, we present methods to reduce the data set size while preserving visual quality. Our framework supports volume rendering and surface rendering for data inspection and analysis. Furthermore, we enable a zoom-on-demand approach, where a selected volumetric region is reloaded with higher details. Finally, we evaluated the WAVE framework using a data set from the entomology science research.
This paper investigates clustering techniques as a method of organizing image databases to support popular visual management functions such as searching, browsing and navigation. Different types of hierarchical agglom...
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ISBN:
(纸本)0819429880
This paper investigates clustering techniques as a method of organizing image databases to support popular visual management functions such as searching, browsing and navigation. Different types of hierarchical agglomerative clustering techniques are studied as a method of organizing features spaces as well as summarizing image groups by the selection of a few appropriate representatives. Retrieval performance using both single and multiple level hierarchies are experimented with and the algorithms show an interesting relationship between the top k correct retrievals and the number of comparisons required. Some arguments are given to support the use of such cluster-based techniques for managing distributed image databases.
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