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Surface carving-based automatic volume data reduction

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Abstract

Many fields, such as medicine and biology, are producing an increasingly large volume using high-resolution digital imaging techniques, and this makes effective data analysis and visualization of these volumes more and more difficult. Volume reduction, decreasing the volume size, is one of the promising directions to solve this challenge for interactive volume visualization. In this paper, we present an automatic volume data reduction method called surface carving. It intelligently removes contextual voxels while preserving important features, and finally generates an optimal volume at the desired reduction size/rate. For large volume data sets, a multilevel banded method is introduced to gracefully overcome the memory limit and speed up volume reduction. We compare our technique with traditional cropping and scaling approaches and demonstrate the effectiveness and efficiency of our method with several volume data sets.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments. This work was partially supported by 863 Program Project 2012AA12A404, National Natural Science Foundation of China No. 61472354 and the Fundamental Research Funds for the Central Universities (2013QNA5010). The data sets are courtesy of Osirix DICOM, VolVis, and Siemens Medical Systems.

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  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, People’s Republic of China

    Qichao Wang, Yubo Tao & Hai Lin

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  1. Qichao Wang
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  2. Yubo Tao
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  3. Hai Lin
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Correspondence to Yubo Tao.

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Wang, Q., Tao, Y. & Lin, H. Surface carving-based automatic volume data reduction. Vis Comput 31, 1459–1470 (2015). https://doi.org/10.1007/s00371-014-1026-2

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