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.