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.%为了进一步提高工业CT图像分割的精确度和运行速度,提出基于灰度-梯度二维指数交叉熵和混沌萤火虫群优化的阈值图像分割方法.运用最小指数交叉熵进行阈值分割,解决了Shannon熵在零点处无定义的问题.采用灰度-梯度二维直方图能更加准确地实现目标和背景的划分,提高算法的抗噪性.此外,为了更好地进行阈值的全局搜索,利用立方映射生成的混沌序列来初始化萤火虫的位置;采用基于立方映射的混沌萤火虫群优化算法搜寻最佳的二维阈值,以进一步提升运算速度.最后,与基于萤火虫算法的二维熵法、基于遗传算法的二维最小交叉熵法作了比较.实验结果表明,该方法在分割效果和处理速度上有明显优势.
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