提出利用极端学习机算法(ELM)在线构建像素分类模型分割白细胞图像。训练阶段根据白细胞核深染色的特点,先利用一个Mean-shift过程在RGB空间定位白细胞核区;再经核区形态学膨胀,得到一个熵与面积之比最大的区域作为正样本候选区域, 而此区域外像素则作为负样本候选区域;通过正负样本像素抽样组成训练集,能在线训练得到一个两分类ELM模型。多次抽样得到的训练集可以产生多个ELM模型。测试阶段利用上述ELM模型集成分类全体像素,可实现白细胞自动分割。与传统图像分割算法相比,本文方法基本无参数调整,可自适应光照和染色条件导致的图像颜色变化,分割效果好。相关实验结果表明算法的有效性。

In this paper we present a two-stage method to segment white blood cell imags by a pixel classification model that is trained online using an extreme learning machine (ELM). During the training stage, we first locate leukocyte nucleus by mean-shift algorithm in the RGB color space. Then we dilate the leukocyte nucleus until the maximum ratio of entropy and area of the nucleus region occurs. The region including the nucleus could be regarded as positive candidate region for sampling. While the other regions excluding the positive one, are regarded as negative candidate regions. A two-class ELM could be trained with the training set via learning by sampling. Different training sets produce multiple models of ELM. In the test stage, multiple models of the ELM can be integrated to classify pixels in order to extract leukocytes. The proposed algorithm does not need to change any parameter during run-time. It is very robust to various staining and to the illumination in cell imaging. Experimental results demonstrate the effectiveness of the method.