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Clustering has become an unavoidable step in big data analysis. It may be used to arrange data into a compact format, making operations on big data manageable. However, clustering of big data requires not only the capability of handling data with large volume and high dimensionality, but also the ability to process streaming data, all of which are less developed in most current algorithms. Furthermore,...
As growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases, the low-dose CT imaging is becoming a hot issue, how to minimize the radiation exposure level while maintaining the diagnostic performance. In previous work, we have proposed a framework that is working in conjunction with image-based database. It restores a low-dose image with modified non-local...
Clustering is an important preparation step in big data processing. It may even be used to detect redundant data points as well as outliers. Elimination of redundant data and duplicates can serve as a viable means for data reduction and it can also aid in sampling. Visual feedback is very valuable here to give users confidence in this process. Furthermore, big data preprocessing is seldom interactive,...
Color mapping and semitransparent layering play an important role in many visualization scenarios, such as information visualization and volume rendering. The combination of color and transparency is still dominated by standard alpha-compositing using the Porter-Duff over operator which can result in false colors with deceiving impact on the visualization. Other more advanced methods have also been...
The DIRECT approach for 3-D Time-of-Flight (TOF) PET reconstruction performs all iterative predictor-corrector operations directly in image space. A computational bottleneck here is the convolution with the long TOF (resolution) kernels. Accelerating this convolution operation using GPUs is very important especially for spatially variant resolution kernels, which cannot be efficiently implemented...
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