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

Abstract

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.Index Terms -Classification, Histogram of volume data, minimum cross entropy, partition of the histogram

State of the Art in Transfer Functions for Direct Volume Rendering

“…One‐dimensional TFs are adequate in many cases of simulation data where measurement noise is low or even non‐existent. Other examples include industrial CT scans, such as in Li et al [LZY*07b], where different materials of interest have few overlapping intensity ranges. For medical image data, the 1D TF is often inadequate as tissues have significant overlap in the intensity range, as described by Lundström et al [LLY06a].…”

“…Li et al . [LZY*07a, LZY*07b] concentrate on industrial CT applications and 1D histograms. They propose using stochastic methods to differentiate between the clusters in the histogram.…”