目的 为实现高通量dPCR荧光图像阳性点高精确度分类，提出一种改进的K-means高通量dPCR荧光图像分类算法。方法 首先，将预处理后的荧光图像进行像素灰度值统计，依据图像亮度自适应选择波峰波谷作为聚类中心，通过马氏距离度量确定像素簇类;然后，将粗分类结果进行开、闭运算及删除小面积对象等形态学处理;最后，利用3次连通域统计方法完成细分类、位置标识和计数。结果 选取4种通道825幅荧光图像进行检验，平均精确率达到99.06%，召回率达到98.97%，分类效果良好。结论 文中提出的改进K-means分类算法可以实现对高通量dPCR荧光图像的高精度分类和计数，对其他荧光图像分类识别具有一定借鉴意义。

The work aims to propose an improved K-means high-throughput dPCR fluorescent image classification algorithm to achieve high-precision classification of high-throughput dPCR fluorescent image positive points. Firstly, the gray value of the preprocessed fluorescent image was counted, and wave peak and valley adaptively were selected according to image brightness to determine cluster center. The pixel clusters were determined by Mahalanobis distance. Then, the broad classification results were processed by morphology opening-and-closing operations and deleting small area objects. Finally, the fine classification, location identification and counting were completed with the third connected domain statistics. In the experiment, 825 fluorescence images of four channels were selected to test. The average accuracy was 99.06%, and recall rate was 98.97%, showing good classification effect. The classification algorithm of improved K-means proposed in this paper can achieve high-precision classification and counting of high-throughput dPCR fluorescent images, and can be used for reference to other fluorescent image classification and recognition.