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首页> 美国卫生研究院文献>Computational and Mathematical Methods in Medicine >Simultaneous Segmentation of Leukocyte and Erythrocyte in Microscopic Images Using a Marker-Controlled Watershed Algorithm
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Simultaneous Segmentation of Leukocyte and Erythrocyte in Microscopic Images Using a Marker-Controlled Watershed Algorithm

机译：标记控制的分水岭算法在显微图像中白细胞和红细胞的同时分割

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摘要

The density or quantity of leukocytes and erythrocytes in a unit volume of blood, which can be automatically measured through a computer-based microscopic image analysis system, is frequently considered an indicator of diseases. The segmentation of blood cells, as a basis of quantitative statistics, plays an important role in the system. However, many conventional methods must firstly distinguish blood cells into two types (i.e., leukocyte and erythrocyte) and segment them in independent procedures. In this paper, we present a marker-controlled watershed algorithm for simultaneously extracting the two types of blood cells to simplify operations and reduce computing time. The method consists of two steps, that is, cell nucleus segmentation and blood cell segmentation. An image enhancement technique is used to obtain the leukocyte marker. Two marker-controlled watershed algorithms are based on distance transformation and edge gradient information to acquire blood cell contour. The segmented leukocytes and erythrocytes are obtained simultaneously by classification. Experimental results demonstrate that the proposed method is fast, robust, and efficient.
机译：单位体积血液中白细胞和红细胞的密度或数量可以通过基于计算机的显微图像分析系统自动测量，通常被认为是疾病的指标。血细胞的分割，作为定量统计的基础，在系统中起着重要的作用。但是，许多常规方法必须首先将血细胞区分为两种类型（即白血球和红血球），并以独立的程序将它们分割。在本文中，我们提出了一种标记控制的分水岭算法，用于同时提取两种类型的血细胞，从而简化操作并减少计算时间。该方法包括两个步骤，即细胞核分割和血细胞分割。图像增强技术用于获得白细胞标记。两种基于标记的分水岭算法基于距离变换和边缘梯度信息来获取血细胞轮廓。通过分类同时获得分段的白细胞和红细胞。实验结果表明，该方法快速，鲁棒，高效。

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