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Advances in Multimedia Volume 2018 ,2018-11-01
Region Space Guided Transfer Function Design for Nonlinear Neural Network Augmented Image Visualization
Research Article
Fei Yang 1 , 2 Xiangxu Meng 1 JiYing Lang 2 Weigang Lu 3 Lei Liu 4
Show affiliations
DOI:10.1155/2018/7479316
Received 2018-07-06, accepted for publication 2018-09-12, Published 2018-09-12
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摘要

Visualization provides an interactive investigation of details of interest and improves understanding the implicit information. There is a strong need today for the acquisition of high quality visualization result for various fields, such as biomedical or other scientific field. Quality of biomedical volume data is often impacted by partial effect, noisy, and bias seriously due to the CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) devices, which may give rise to an extremely difficult task of specifying transfer function and thus generate poor visualized image. In this paper, firstly a nonlinear neural network based denoising in the preprocessing stage is provided to improve the quality of 3D volume data. Based on the improved data, a novel region space with depth based 2D histogram construction method is then proposed to identify boundaries between materials, which is helpful for designing the proper semiautomated transfer function. Finally, the volume rendering pipeline with ray-casting algorithm is implemented to visualize several biomedical datasets. The noise in the volume data is suppressed effectively and the boundary between materials can be differentiated clearly by the transfer function designed via the modified 2D histogram.

授权许可

Copyright © 2018 Fei Yang et al. 2018
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

图表

The architecture of the MLP network.

Preprocessing results of tooth dataset. From top to bottom: original slice data, denoised images by the nonlinear enhancement algorithm, and denoised images by MLP. (a) Original slice data; (b) nonlinear enhancement denoised result; (c) MLP denoised result.

LH histogram and rendering result of the original tooth data and MLP denoised data: (a) LH histogram and the corresponding rendering result of original data and (b) LH histogram and corresponding rendering result of denoised data.

LH histogram and rendering result of the original tooth data and MLP denoised data: (a) LH histogram and the corresponding rendering result of original data and (b) LH histogram and corresponding rendering result of denoised data.

LH histogram and corresponding rendering result with nonlinear enhanced tooth dataset through the conventional and region criteria based method: (a) LH histogram based on conventional method with gradient threshold of 5 and the corresponding rendering result and (b) LH histogram based on region criteria based method and the rendering result.

LH histogram and corresponding rendering result with nonlinear enhanced tooth dataset through the conventional and region criteria based method: (a) LH histogram based on conventional method with gradient threshold of 5 and the corresponding rendering result and (b) LH histogram based on region criteria based method and the rendering result.

Rendering result of MLP augmented tooth dataset with two methods: (a) rendering result based on region criteria based method and (b) rendering result based on depth enhanced method.

Rendering result of MLP augmented tooth dataset with two methods: (a) rendering result based on region criteria based method and (b) rendering result based on depth enhanced method.

Visualization result of augmented sheep heart dataset with region space guided transfer function: (a) the original sheep heart data; (b) the denoised data; (c) rendering result of sheep heart with the denoised data.

Visualization result of augmented sheep heart dataset with region space guided transfer function: (a) the original sheep heart data; (b) the denoised data; (c) rendering result of sheep heart with the denoised data.

Visualization result of augmented sheep heart dataset with region space guided transfer function: (a) the original sheep heart data; (b) the denoised data; (c) rendering result of sheep heart with the denoised data.

通讯作者

Xiangxu Meng.School of Computer Science and Technology, Shandong University, Jinan 250101, China, sdu.edu.cn.mxx@sdu.edu.cn

推荐引用方式

Fei Yang,Xiangxu Meng,JiYing Lang,Weigang Lu,Lei Liu. Region Space Guided Transfer Function Design for Nonlinear Neural Network Augmented Image Visualization. Advances in Multimedia ,Vol.2018(2018)

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参考文献
[1] F.-Y. Tzeng, E. B. Lum, K-L. Ma. A novel interface for higher-dimensional classification of volume data. :505-512. DOI: 10.1109/TITB.2012.2205011.
[2] J. Holub, E. Winer. (2017). Enabling Real-Time Volume Rendering of Functional Magnetic Resonance Imaging on an iOS Device. Journal of Digital Imaging.30(6):738-750. DOI: 10.1109/TITB.2012.2205011.
[3] L. Zhang, K. Wang, F. Yang, W. Lu. et al.(2016). A Visualization System for Interactive Exploration of the Cardiac Anatomy. Journal of Medical Systems.40(6). DOI: 10.1109/TITB.2012.2205011.
[4] P. Šereda, A. Vilanova, F. A. Gerritsen. Automating transfer function design for volume rendering using hierarchical clustering of material boundaries. :243-250. DOI: 10.1109/TITB.2012.2205011.
[5] Q. Zhang, R. Eagleson, T. M. Peters. (2011). Volume visualization: A technical overview with a focus on medical applications. Journal of Digital Imaging.24(4):640-664. DOI: 10.1109/TITB.2012.2205011.
[6] J. Kniss, G. Kindlmann, C. Hansen. (2002). Multidimensional transfer functions for interactive volume rendering. IEEE Transactions on Visualization and Computer Graphics.8(3):270-285. DOI: 10.1109/TITB.2012.2205011.
[7] P. Thunberg, A. Kähäri. (2011). Visualization of Through-Plane Blood Flow Measurements Obtained from Phase-Contrast MRI. Journal of Digital Imaging.24(3):470-477. DOI: 10.1109/TITB.2012.2205011.
[8] F.-Y. Tzeng, K.-L. Ma. A cluster-space visual interface for arbitrary dimensional classification of volume data. :17-24. DOI: 10.1109/TITB.2012.2205011.
[9] H. Pfister, B. Lorensen, C. Bajaj, G. Kindlmann. et al.(2001). The transfer function bake-off. IEEE Computer Graphics and Applications.21(3):16-22. DOI: 10.1109/TITB.2012.2205011.
[10] E. B. Lum, K.-L. Ma. Lighting transfer functions using gradient aligned sampling. :289-296. DOI: 10.1109/TITB.2012.2205011.
[11] G. Kindlmann, R. Whitaker, T. Tasdizen, T. Möller. et al.Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications. :513-520. DOI: 10.1109/TITB.2012.2205011.
[12] J. Hladůvka, A. König, E. Gröller. Curvature-based transfer functions for direct volume rendering. .16:58-65. DOI: 10.1109/TITB.2012.2205011.
[13] Q. Zhang, R. Eagleson, T. M. Peters. (2012). GPU-based visualization and synchronization of 4-D cardiac MR and ultrasound images. IEEE Transactions on Information Technology in Biomedicine.16(5):878-890. DOI: 10.1109/TITB.2012.2205011.
[14] S. Takahashi, Y. Takeshima, I. Fujishiro. (2004). Topological volume skeletonization and its application to transfer function design. Graphical Models.66(1):24-49. DOI: 10.1109/TITB.2012.2205011.
[15] G. Kindlmann, J. W. Durkin. Semi-automatic generation of transfer functions for direct volume rendering. :79-86. DOI: 10.1109/TITB.2012.2205011.
[16] M. Levoy. (1988). Display of surfaces from volume data. IEEE Computer Graphics and Applications.8(3):29-37. DOI: 10.1109/TITB.2012.2205011.
[17] C. D. Correa, K.-L. Ma. (2008). Size-based transfer functions: a new volume exploration technique. IEEE Transactions on Visualization and Computer Graphics.14(6):1380-1387. DOI: 10.1109/TITB.2012.2205011.
[18] T. S He, L. C. Hong, A. Kaufman, et al. et al.Generation of transfer functions with stochastic search techniques. :227-234. DOI: 10.1109/TITB.2012.2205011.
[19] L. Zhang, K. Wang, H. Zhang, W. Zuo. et al.(2014). Illustrative cardiac visualization via perception-based lighting enhancement. Journal of Medical Imaging and Health Informatics.4(2):312-316. DOI: 10.1109/TITB.2012.2205011.
[20] J. Marks, B. Mirtich, B. Andalman, H. Pfister. et al.Design Galleries: A general approach to setting parameters for computer graphics and animation. :389-400. DOI: 10.1109/TITB.2012.2205011.
[21] D. S. Ebert, D. G. Heath, B. S. Kuszyk, L. Edwards. et al.(1998). Evaluating the potential and problems of three-dimensional computed tomography measurements of arterial stenosis. Journal of Digital Imaging.11(3):151-157. DOI: 10.1109/TITB.2012.2205011.
[22] Y.-T. Ching, C.-L. Chang. (2002). A volume rendering technique to generate a very large wide-angle endoscopeic view. Journal of Medical and Biological Engineering.22(2):109-112. DOI: 10.1109/TITB.2012.2205011.
[23] F.-Y. Tzeng, E. B. Lum, K.-L. Ma. (2005). An intelligent system approach to higher-dimensional classification of volume data. IEEE Transactions on Visualization and Computer Graphics.11(3):273-283. DOI: 10.1109/TITB.2012.2205011.
[24] F. Yang, W. G. Lu, L. Zhang, W. M. Zuo. et al.Fusion visualization for cardiac anatomical and ischemic models with depth weighted optic radiation function. :937-940. DOI: 10.1109/TITB.2012.2205011.
[25] L. Zhang, C. Gai, K. Wang, W. Lu. et al.GPU-based high performance wave propagation simulation of ischemia in anatomically detailed ventricle. :469-472. DOI: 10.1109/TITB.2012.2205011.
[26] M. S. Hsieh, F. P. Lee, M. D. Tsai. (2010). A virtual reality ear ossicle surgery simulator using three-dimensional computer tomography. Journal of Medical and Biological Engineering.30(1):57-63. DOI: 10.1109/TITB.2012.2205011.
[27] G. Gerig, O. Kubler, R. Kikinis, F. A. Jolesz. et al.(1992). Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging.11(2):221-232. DOI: 10.1109/TITB.2012.2205011.
[28] R. Huang, . Kwan-Liu Ma, P. McCormick, W. Ward. et al.Visualizing industrial CT volume data for nondestructive testing applications. :547-554. DOI: 10.1109/TITB.2012.2205011.
[29] I. Fujishiro, T. Azuma, Y. Takeshima. Automating transfer function design for comprehensible volume rendering based on 3D field topology analysis. :467-470. DOI: 10.1109/TITB.2012.2205011.
[30] P. Perona, J. Malik. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence.12(7):629-639. DOI: 10.1109/TITB.2012.2205011.
[31] S. Roettger, M. Bauer, M. Stamminger. Spatialized transfer functions. :271-278. DOI: 10.1109/TITB.2012.2205011.
[32] H. C. Burger, C. J. Schuler, S. Harmeling. Image denoising: Can plain neural networks compete with BM3D?. :2392-2399. DOI: 10.1109/TITB.2012.2205011.
[33] P. Šereda, A. V. Bartrolí, I. W. O. Serlie, F. A. Gerritsen. et al.(2006). Visualization of boundaries in volumetric data sets using lh histograms. IEEE Transactions on Visualization and Computer Graphics.12(2):208-217. DOI: 10.1109/TITB.2012.2205011.
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