Classification for Volume Rendering of Industrial CT Based on Minimum Cross Entropy

The classification step used to assign the appropriate opacity to each voxel is very important in the volume rendering. A new classification algorithm for volume data, which is based on minimum cross entropy, is proposed in this paper. Firstly, the volume data is constructed from a series of sequential two-dimension industrial CT images and the histogram of the volume data is computed. Secondly, the histogram of the volume data is partitioned into different subsections through calculating the accumulated histogram of volume data according to the number of object classes. Thirdly, in each subsection a threshold is computed based on minimum cross entropy. Finally, the opacity of each voxel is assigned by a transfer function, which is split into subsection by these thresholds. Two experimental results from industrial CT images are presented. One is the volume data of vane and the other is volume data of electric drill, from which we can see that the volume rendering results for industrial components are smooth and the image data isn't lost when rotating great angles. In the mean time, this algorithm makes the simulated disassembly of the industrial components performed on the computer successfully.

[1]  B S Kuszyk,et al.  CT angiography with volume rendering: advantages and applications in splanchnic vascular imaging. , 1996, Radiology.

[2]  Zhiwu Lu,et al.  A Regularized Minimum Cross-entropy Algorithm on Mixture of Experts for Curve Detection , 2005, 2005 International Conference on Neural Networks and Brain.

[3]  Chun-hung Li,et al.  Minimum cross entropy thresholding , 1993, Pattern Recognit..

[4]  Marc Levoy,et al.  Display of surfaces from volume data , 1988, IEEE Computer Graphics and Applications.

[5]  Jizhou Sun,et al.  Automatic classification of MRI images for three-dimensional volume reconstruction by using general regression neural networks , 2003, 2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515).

[6]  Huazhong Shu,et al.  Modified minimum cross-entropy algorithm for image reconstruction using total variation regularization , 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

[7]  V Argiro,et al.  Perspective volume rendering of CT and MR images: applications for endoscopic imaging. , 1996, Radiology.

[8]  Kwan-Liu Ma,et al.  A novel interface for higher-dimensional classification of volume data , 2003, IEEE Visualization, 2003. VIS 2003..

[9]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[10]  C. H. Lie,et al.  Segmentation of die patterns using minimum cross entropy , 1992, Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation.

[11]  A. D. Brink,et al.  Minimum cross-entropy threshold selection , 1996, Pattern Recognit..

[12]  Yuriko Takeshima,et al.  Automating transfer function design for comprehensible volume rendering based on 3D field topology analysis , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[13]  Huazhong Shu,et al.  A edge-preserving minimum cross-entropy algorithm for PET image reconstruction using multiphase level set method , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[14]  Kwan-Liu Ma,et al.  ISpace: interactive volume data classification techniques using independent component analysis , 2002, 10th Pacific Conference on Computer Graphics and Applications, 2002. Proceedings..

[15]  M. Levoy.  Volume Rendering Display of Surfaces from Volume Data , 1988 .

[16]  Carl-Fredrik Westin,et al.  Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering , 2000, IEEE Trans. Vis. Comput. Graph..

[17]  Pat Hanrahan,et al.  Volume Rendering , 2020, Definitions.