Abstract: Due to considering the gray level spatial distribution information, some image segmentation technologies based on entropy threshold can enhance the thresholding segmentation performance. However, they still cannot distinguish image edges and noise well. Even though GLGM (gray-level & gradient-magnitude) entropy can effectively solve the problem, it cannot segment effectively multi-objective and complex image. So, this paper proposes image segmentation with multi-threshold of GLGM entropy based on genetic algorithm. In the proposed method, integral figure is introduced in order to make threshold searching dimension from original O(9×L) to O(L), and the single threshold segmentation of GLGM entropy is further extended to multi-threshold segmentation. Lastly, the real-code-GA is used to search the best thresholds. The simulation results show that this method can be effectively applied for the multi-threshold segmentation of complex images.

Key words: genetic algorithm, gray-level &, gradient-magnitude entropy, multi-threshold, image segmentation, integral figure

摘要： 一些基于熵的阈值图像分割技术考虑了空间信息，从而能够提高阈值分割的性能，但是仍然不能较好地区分边缘和噪声。尽管灰度-梯度（gray-level & gradient-magnitude，GLGM）熵算法能有效地解决以上问题，但是针对多目标和复杂图像却不能有效地分割。为此，提出了一种基于遗传算法（genetic algorithm，GA）的GLGM熵多阈值快速分割方法。该方法应用积分图思想将GLGM熵算法阈值搜索空间从O(9×L)降到O(L)，并将GLGM熵算法从单阈值拓展到多阈值。最后应用基于实数编码的遗传算法搜索GLGM熵多阈值的最佳阈值。仿真结果表明，该方法能够实现图像的快速多阈值分割，适合复杂图像分割。

关键词: 遗传算法, 灰度-梯度熵, 多阈值, 图像分割, 积分图