光学显微结核杆菌图像的区域分割算法

A segmentation algorithm for optical microscopic mycobacterium tuberculosis images based on region segmentation

为提高结核杆菌目标的分割精度,提出了一种区域级的光学显微结核杆菌图像分割算法。通过顶帽—底帽变换增强彩色图像对比度,融合图像局部特征和全局信息计算图像梯度,利用分水岭算法实现对图像的初始分割;对分割区域采用相邻区域最大相似度准则进行合并,从而得到完整的目标区域;根据结核杆菌图像的特点,通过分析结核杆菌目标区域的颜色特性,采用多阈值分割的方法滤除区域中的杂质,实现对结核杆菌目标的分割。实验结果表明,可以分割出目标对比度低以及饱和度过低的结核杆菌目标,并且对不同染色背景的图像均能取得较好的分割结果。

To improve the segmentation accuracy of mycobacterium tuberculosis （MTB ） objects,a segmentation algorithm for optical microscopic MTB images in region level was proposed.Top-bottom hat transform was used to enhance the contrast of the color images,and the image gradients were computed by comprising local features and global information of the images.Watershed algorithm was employed to implement the initial segmentation.Segmented regions were then merged by using the maximum similarity criterion in adjacent regions in order to obtain integrated object regions.The method of multi-threshold segmentation in terms of the color characteristics of MTB object regions was adopted to filter the impurities and to realize the segmentation of MTB objects.Experimental results indicate that the proposed algorithm can segment MTB objects which have low contrast and saturation and can obtain well-segmented results for images in different dyeing backgrounds.