研究了白细胞图像特征提取和分类识别方法，以提高白细胞图像的正确识别率.针对细胞纹理特征的提取，采用改进的局部模糊模式提取白细胞图像的纹理特征，通过对局部二值模式中阈值参量的模糊化，建立了基于局部模糊模式的纹理特征提取算法.算法中引入“统一模式”方法，使提取的特征维度降低为10，且具有旋转不变性.通过有向无环图方法建立支持向量机组合分类器，对CellAtlas的100幅白细胞图像进行了分类识别的实验.实验结果表明：改进的局部模糊模式算法精简了纹理特征数量，起到了“去伪存真”的作用，在含有噪音的白细胞图像分类识别中表现出优良的性能，使提取的特征具有更好的“鲁棒性”，并且具有运行时间短、效率高的特点，白细胞的正确识别率达到了93%.改进的支持向量机分类器表现出高效的分类效果，对小样本分析具有更好的特性.

Leukocyte image feature extraction and classification are studied to improve the correct recognition rate of leukocyte image. For cell texture feature extraction, leukocyte image texture features are extracted by using the improved local fuzzy pattern,and the texture feature extraction method based on local fussy pattern (LFP) was proposed by making the threshold parameter fuzzy in local binary pattern (LBP). The algorithm introduces in uniform pattern to make the extracted feature dimension decrease to 10 with rotation invariance. The classification of 100 CellAtlas’s white blood cells images was tested with a support vector machine combination classifier established by a directed acyclic graph method. Experimental results show that: the improved local fuzzy pattern algorithm simplifies texture feature quantity to realize “discard the false and retain the true”.The leukocyte image classification and recognition with noise exhibits excellent performance, so that the extracted features have better Robustness. And it has a short running time, high efficiency, leukocyte correct recognition rate is up to 93%. Improved support vector machine classifier shows efficient classification effect,and has better characteristics to small sample analysis.