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Milk duct segmentation in microscopic HE images of breast cancer tissues

机译:微观乳腺癌组织图像中的牛奶管道分割

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The aim of the paper is to recognize and extract the milk duct in haematoxylin and eosin (HE) stained breast cancer tissues. The paper presents the modified K-means approach to segmentation of the milk duct in HE stained images. Instead of using single pixels we propose to consider the defined region of pixels in the process. Thanks to such modification more accurate extraction of the milk ducts has been achieved. To compare the results in a numerical way the GT images prepared by the medical expert have been subtracted from the corresponding images created by the segmentation methods. The numerical experiments performed for many preparations have confirmed the superiority of such approach. The proposed method has allowed reducing significantly the error of duct segmentation in comparison to the classical K-means approaches. The results show, that our method is superior to the standard K-means and to the K-means preceded by averaging or Gaussian filtration at different size of filtration mask.
机译:本文的目的是识别和提取血红素和曙红(HE)染色乳腺癌组织中的牛奶管道。本文提出了修改的K-Means方法来分割他染色图像中的牛奶管道。而不是使用单个像素我们建议考虑过程中的定义像素区域。由于这种修改,更准确地实现了牛奶管道的提取。为了以数值方式比较结果,通过由分段方法创建的相应图像中减去由医学专家准备的GT图像。对许多制剂进行的数值实验证实了这种方法的优越性。所提出的方法使得与经典K-MeSCE方法相比,允许显着降低管道分割的误差。结果表明,我们的方法优于标准的K型k型,并以不同尺寸的过滤掩模的平均或高斯过滤之前的k-in。

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