Recognition of dense fluorescent droplets using an improved watershed segmentation algorithm
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摘要: 基于数字微滴图像检测法的数字聚合酶链式反应(PCR)在检测时获取的荧光微滴图像呈密集分布、具有低亮度、低对比度等特点，导致其识别正确率较低。为了实现对密集分布的荧光微滴的正确识别，本文提出一种基于改进的分水岭分割算法的荧光微滴识别方法，首先利用直方图均衡化和高斯滤波对图像进行预处理，然后使用局部自适应阈值分割从背景中提取目标，降低对图像灰度信息的依赖，最后结合微滴形状类圆、尺寸较均匀的特点定义微滴黏连度函数，降低了分水岭分割中的错误分割比例。对比实验表明，与传统的基于距离变换分水岭分割法相比较，本文算法的正确率为97.34%，高于对照方法的85.9%，验证了本文算法的优越性。Abstract: Fluorescent droplet images acquired during droplet digital Polymerase Chain Reaction(PCR) detection have dense distribution, low brightness and low contrast, resulting in poor recognition accuracy. In order to correctly identify densely distributed fluorescent droplets, a fluorescent droplet recognition method based on an improved watershed segmentation algorithm is proposed. First, the image is preprocessed using histogram equalization and Gauss filtering, then the local adaptive threshold segmentation method is used to extract the targets from the background, thereby reducing the dependence on image gray level information. Finally, the algorithm combines the prior knowledge of the droplets with a circular and uniform size to define the droplet adhesion function, which reduces the error rate in the watershed segmentation. The experiment results show that compared with the traditional distance-based watershed segmentation method, the accuracy of the proposed algorithm is 97.34%, which is higher than the 85.9% accuracy of its counterpart.
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图 2 预处理后的示例图像(a)及其灰度分布(b)
Figure 2. Preprocessed sample image(a) and its gray distribution(b)
图 3 本文方法分割后的示例图像(a)和Otsu法分割的示例图像(b)
Figure 3. Segmented sample images obtained by proposed method(a) and Otsu method(b)
图 4 荧光微滴自动识别算法流程图
Figure 4. Workflow of fluorescent droplet automatic recognition algorithm
表 1 微滴分割算法性能对照
Table 1. Performance comparison of different droplet segmentation methods
方法 阳性欠分割率/% 阳性过分割率/% 阴性欠分割率/% 阴性过分割率/% 平均正确率/% 本文方法 0.24 0.62 5.32 0 97.34 对照方法 0.81 0.20 21.59 1.06 85.90 
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