基于遗传算法的灰度-梯度熵多阈值图像分割

摘要

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 dis-tinguish 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.%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。