Zhao Jianwei, Zhang Minshu, Zhou Zhenghua, Chu Jianjun, Cao Feilong
Department of Applied Mathematics, College of Science, China Jiliang University, Hangzhou, 310018, Zhejiang Province, People's Republic of China.
Jiashan Jasdaq Medical Device Co., Ltd., Jiashan, 314100, Zhejiang Province, People's Republic of China.
Med Biol Eng Comput. 2017 Aug;55(8):1287-1301. doi: 10.1007/s11517-016-1590-x. Epub 2016 Nov 7.
The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.
白细胞（WBC，也称为白细胞）的检测和分类是一个热点问题，因为其在疾病诊断中具有重要应用。如今，血细胞的形态分析由熟练的操作人员手动进行，这导致了一些缺点，如分析速度慢、准确性不标准以及对操作人员技能的依赖。尽管已经有许多论文分别研究白细胞的检测或分类，但很少有论文将它们结合起来考虑。本文提出了一种从外周血图像中自动检测和分类白细胞的系统。它首先基于颜色R、B的简单关系和形态学操作，提出了一种从显微镜图像中检测白细胞的算法。然后，应用粒度特征（成对旋转不变共生局部二值模式，PRICoLBP特征）和支持向量机首先将嗜酸性粒细胞和嗜碱性粒细胞与其他白细胞进行分类。最后，使用卷积神经网络自动从白细胞中提取高级特征，并将随机森林应用于这些特征以识别其他三种白细胞：中性粒细胞、单核细胞和淋巴细胞。在Cellavison数据库和ALL-IDB数据库上进行的一些检测实验表明，我们提出的检测方法几乎比迭代阈值法具有更好的效果，且耗时更少，一些分类实验表明，我们提出的分类方法几乎比其他一些方法具有更高的准确率。
