Journal of Visualization

, Volume 21, Issue 4, pp 637–647 | Cite as

A novel robust color gradient estimator for photographic volume visualization

  • Bin Zhang
  • Zhiguang Zhou
  • Yubo Tao
  • Hai LinEmail author
Regular Paper


Photographic volume visualization has been widely applied in various fields ranging from medicine to biology. Different from scalar volume data, photographic volume data are directly captured by means of the modern cryo-imaging systems. The voxels are recorded as RGB vectors, which makes it difficult to estimate accurate gradient for the shading and the design of transfer functions. In this paper, we propose a robust color gradient estimation method to produce accurate and robust gradient results for photographic volumes. First, a robust color morphological gradient (RCMG) operator is employed to estimate the gradient in a dominant direction and the low-pass filters are then applied to reduce the effects of noises. Then, an aggregation operator is applied to estimate the accurate gradient directions and the gradient magnitudes. Based on the obtained color gradients, the shading effects of internal materials are enhanced and the features can be better specified in a 2D transfer function space. At last, the effectiveness of the robust gradient estimation for photographic volume is demonstrated based on a large number of experimental rendering results, especially for those noisy photographic volume data sets.

Graphical abstract


Photographic volume Color gradient Volume rendering Transfer function 



The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by NSF of China Project Nos. 61303133, 61472354, the National Statistical Scientific Research Project No. 2015LD03, the China Postdoctoral Science Foundation No. 2015M571846, the Zhejiang Science and Technology Plan of China No. 2014C31057, and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant 2014BAK14B01.


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Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina
  2. 2.School of InformationZhejiang University of Finance and EconomicsHangzhouChina

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