A python computer package is developed to segment and analyze scanning electron microscope (SEM) images of scaffolds for bone tissue engineering. The method requires only a portion of an SEM image to be labeled and used for training. The algorithm is then able to detect the pore characteristics for other SEM images acquired at different ambient conditions from different scaffolds with the same material as the labeled image. The quality of SEM images is first enhanced using histogram equalization. Then, a global thresholding method is used to perform the image analysis. The thresholding values for the SEM images are obtained using genetic algorithm (GA). The image analysis results include pore distributions of pore size, pore elongation and pore orientation. The results agree satisfactorily with the experimental data for the chitosan–alginate porous scaffolds considered. Applications of the method developed for image segmentation is not limited to scaffold pore structure analysis. The method can also be used for any SEM image containing multiple objects such as different types of cells and subcellular components.

开发了一个 python 计算机包来分割和分析骨组织工程支架的扫描电子显微镜 (SEM) 图像。该方法只需要标记 SEM 图像的一部分并用于训练。然后，该算法能够检测在不同环境条件下从具有与标记图像相同的材料的不同支架获取的其他 SEM 图像的孔隙特征。首先使用直方图均衡来增强 SEM 图像的质量。然后，使用全局阈值方法进行图像分析。使用遗传算法 (GA) 获得 SEM 图像的阈值。图像分析结果包括孔径、孔伸长率和孔取向的孔分布。结果与所考虑的壳聚糖-藻酸盐多孔支架的实验数据一致。为图像分割开发的方法的应用不仅限于支架孔结构分析。该方法还可用于包含多个对象（例如不同类型的细胞和亚细胞成分）的任何 SEM 图像。