Abstract: To further improve the segmentation accuracy and processing speed of CT image in
industrial CT detection system, the industrial CT image threshold segmentation was proposed based
on 2-D exponential cross entropy and chaotic glow-worm swarm optimization. By using the
minimum exponential cross entropy for threshold segmentation, the drawback of undefined value at
zero of Shannon entropy was avoided. At the same time, 2-D histogram based on gray-gradient was
taken to partition the object and background precisely in order to improve the anti-noise performance.
In addition, chaotic sequence generated by cube map was used to initiate individual position for easy global searching, and chaotic glow-worm swarm optimization algorithm based cube map was used to
search for 2-D optimal threshold in order to further increase algorithmic speed. Finally, a large
number of experiments on industrial CT images were processed and then the experimental results
were compared with 2-D entropy method based on firefly algorithm and minimum cross entropy
method based on genetic algorithm. The obtained results show that the proposed method has obvious
advantages in segmentation and processing speed.

Key words: image segmentation, industrial CT image, threshold selection, exponential cross entropy;
chaotic glow-worm swarm, cube mapping