Automated segmentation of multiple red blood cells with digital holographic microscopy
Automated segmentation of multiple red blood cells with digital holographic microscopy
复制标题
DOI：
10.1117/1.jbo.18.2.026006
复制
发表时间：
2013-02-01
影响因子：
3.5
通讯作者：
Marquet, Pierre
中科院分区：
文献类型：
--
作者：
Yi, Faliu;Moon, Inkyu;Marquet, Pierre
提出了一种基于标记控制分水岭算法的数字全息显微镜(DHM)自动分割红细胞的方法。利用离轴DHM可以获得RBC的定量相位图像，提供有关每个RBC的一些重要信息，包括大小、形状、体积、血红蛋白含量等。基于标记控制分水岭的分割过程中，最重要的是对内部和外部标记进行准确的定位。这里，我们首先通过最大类间方差算法得到二值图像。然后，对二值图像进行形态运算，得到图像的内部标记。然后将距离变换算法与分水岭算法相结合，生成基于内部标记的外部标记。最后，结合内部和外部标记，对原始梯度图像进行修正，应用分水岭算法。通过适当地识别内部和外部标记，避免了过度分割和不足分割的问题。此外，利用标记控制分水岭结合我们的方法，还可以对RBCs相位图像的内外部分进行分割，从而能够正确地识别内外标记。我们的实验结果表明，该方法在分割红细胞方面取得了很好的效果，因此与红细胞的自动分类相结合将是有帮助的。(C)2013年光学仪器工程师学会[DOI：10.1117/1JBO.18.2.026006]
We present a method to automatically segment red blood cells (RBCs) visualized by digital holographic microscopy (DHM), which is based on the marker-controlled watershed algorithm. Quantitative phase images of RBCs can be obtained by using off-axis DHM along to provide some important information about each RBC, including size, shape, volume, hemoglobin content, etc. The most important process of segmentation based on marker-controlled watershed is to perform an accurate localization of internal and external markers. Here, we first obtain the binary image via Otsu algorithm. Then, we apply morphological operations to the binary image to get the internal markers. We then apply the distance transform algorithm combined with the watershed algorithm to generate external markers based on internal markers. Finally, combining the internal and external markers, we modify the original gradient image and apply the watershed algorithm. By appropriately identifying the internal and external markers, the problems of oversegmentation and undersegmentation are avoided. Furthermore, the internal and external parts of the RBCs phase image can also be segmented by using the marker-controlled watershed combined with our method, which can identify the internal and external markers appropriately. Our experimental results show that the proposed method achieves good performance in terms of segmenting RBCs and could thus be helpful when combined with an automated classification of RBCs. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1JBO.18.2.026006]