A novel robust color gradient estimator for photographic volume visualization
- 177 Downloads
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.
KeywordsPhotographic 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.
- Ercan G, Whyte P (2001) Digital image processing. US Patent 6,240,217Google Scholar
- Gargesha M, Qutaish M, Roy D, Steyer G, Bartsch H, Wilson DL (2009) Enhanced volume rendering techniques for high-resolution color cryo-imaging data. In: SPIE Medical Imaging, International Society for Optics and Photonics, p 72622V. https://doi.org/10.1117/12.813756
- Kindlmann G, Durkin JW (1998) Semi-automatic generation of transfer functions for direct volume rendering. In: Proceedings of the 1998 IEEE symposium on volume visualization, ACM, pp 79–86Google Scholar
- Mittal A, Sofat S, Hancock E (2012) Detection of edges in color images: a review and evaluative comparison of state-of-the-art techniques. In: Proceedings of the third international conference on autonomous and intelligent systems, AIS’12, pp 250–259. https://doi.org/10.1007/978-3-642-31368-4_30
- Morris CJ, Ebert D (2002) Direct volume rendering of photographic volumes using multi-dimensional color-based transfer functions. In: Proceedings of the symposium on data visualisation 2002, Eurographics Association, pp 115-ffGoogle Scholar
- Roettger S, Bauer M, Stamminger M (2005) Spatialized transfer functions. In: Proceedings of the seventh joint eurographics/IEEE VGTC conference on visualization, Eurographics Association, pp 271–278Google Scholar
- Vandenberghe ME, Hrard AS, Souedet N, Sadouni E, Santin MD, Briet D, Carr D, Schulz J, Hantraye P, Chabrier PE, Rooney T, Debeir T, Blanchard V, Pradier L, Dhenain M, Delzescaux T (2016) High-throughput 3D whole-brain quantitative histopathology in rodents. Sci Rep 6:20958. https://doi.org/10.1038/srep20958 CrossRefGoogle Scholar