本文提出了一种新型的基于 mean-shift 聚类算法的人体外周血中白细胞五分类算法，其核心思想是用一种近似人眼的可视化模式对白细胞纹理进行提取。首先利用 mean-shift 聚类算法从白细胞灰度图像中提取一些模式点，然后用其作为区域生长算法的种子点进行区域生长，得到一系列能够在某种程度上可视化地反映纹理的区域块。最后从这些区域块中提取一组参数向量作为白细胞的纹理特征。综合该向量和白细胞形态学特征，用人工神经网络（ANN）成功地完成了对白细胞的五分类识别。用了 1 310 个白细胞图像进行测试，得到中性粒细胞、嗜酸性粒细胞、嗜碱性粒细胞、淋巴细胞、单核细胞的正确识别率分别为 95.4%、93.8%、100%、93.1%、92.4%，证明了该算法的可行性和鲁棒性。 A new leukocyte classification method for recognition of five types of human peripheral blood smear based on mean-shift clustering is proposed. The key idea of the proposed method is to extract the texture features of leukocytes in a visual manner which can benefit from human eyes. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift. Secondly, these feature points are used as seeds of the region growing to expand feature regions which can express texture in visual mode to a certain extent. Finally, a parameter vector of these regions is extracted as the texture feature. Combing the vector with the geometric features of the leukocyte, the five typical classes of leukocytes can be recognized successfully using artificial neural network (ANN). A total number of 1 310 leukocyte images have been tested and the accurate rate of recognition for neutrophil, eosinophil, basophil, lymphocyte and monocyte are 95.4%, 93.8%, 100%, 93.1% and 92.4%, respectively, which shows the feasibility and high robustness of the proposed method.
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