Classification for Volume Rendering of Industrial CT Based on Moment of Histogram

In the volume rendering, the classification step is used to determine voxel visibility and plays an important role. In this paper, a new classification algorithm, which is based on moment of histogram of volume data, is proposed for volume rendering. Firstly, the histogram of volume data is partitioned into different subsections by the number of object classes, which is developed through calculating the entropy of the histogram of volume data. Secondly, in each subsection a threshold is computed according to the moment of the histogram of volume data. Finally, the opacity of each voxel is assigned by a transfer function, which is split by these thresholds into subsection. Three schemes based on the moment of histogram of volume data are applied to determine the threshold and three corresponded results is compared. Examples from industrial CT dataset are presented. In the volume rendering results, industrial components are shown explicitly and can be successfully disassembled in the simulated by the computer.

[1]  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).

[2]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

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

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

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

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

[7]  Feilu Luo,et al.  An improved moment-preserving auto threshold image segmentation algorithm , 2004, International Conference on Information Acquisition, 2004. Proceedings..

[8]  R. E. Sonntag,et al.  Introduction to Thermodynamics: Classical and Statistical , 1971 .

[9]  Chein-I Chang,et al.  Image segmentation by local entropy methods , 1995, Proceedings., International Conference on Image Processing.

[10]  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..

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

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

[14]  Wen-Hsiang Tsai,et al.  Moment-preserving thresholding: a new approach , 1995 .

[15]  H. R. Keshavan,et al.  An optimal multiple threshold scheme for image segmentation , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

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

[17]  Wen-Hsiang Tsai,et al.  Moment-preserving thresolding: A new approach , 1985, Comput. Vis. Graph. Image Process..