Xu Hongming, Lu Cheng, Berendt Richard, Jha Naresh, Mandal Mrinal
IEEE Trans Biomed Eng. 2017 Oct;64(10):2475-2485. doi: 10.1109/TBME.2017.2649485. Epub 2017 Jan 9.
In the diagnosis of various cancers by analyzing histological images, automatic nuclear segmentation is an important step. However, nuclear segmentation is a difficult problem because of overlapping nuclei, inhomogeneous staining, and presence of noisy pixels and other tissue components. In this paper, we present an automatic technique for nuclear segmentation in skin histological images. The proposed technique first applies a bank of generalized Laplacian of Gaussian kernels to detect nuclear seeds. Based on the detected nuclear seeds, a multiscale radial line scanning method combined with dynamic programming is applied to extract a set of candidate nuclear boundaries. The gradient, intensity, and shape information are then integrated to determine the optimal boundary for each nucleus in the image. Nuclear overlap limitation is finally imposed based on a Dice coefficient measure such that the obtained nuclear contours do not severely intersect with each other. Experiments have been thoroughly performed on two datasets with H&E and Ki-67 stained images, which show that the proposed technique is superior to conventional schemes of nuclear segmentation.
在通过分析组织学图像诊断各种癌症时，自动细胞核分割是一个重要步骤。然而，由于细胞核重叠、染色不均匀以及存在噪声像素和其他组织成分，细胞核分割是一个难题。在本文中，我们提出了一种用于皮肤组织学图像中细胞核分割的自动技术。所提出的技术首先应用一组高斯核广义拉普拉斯算子来检测细胞核种子。基于检测到的细胞核种子，应用一种结合动态规划的多尺度径向线扫描方法来提取一组候选细胞核边界。然后整合梯度、强度和形状信息，以确定图像中每个细胞核的最佳边界。最后基于骰子系数度量施加细胞核重叠限制，以使获得的细胞核轮廓不会彼此严重相交。我们已经在两个包含苏木精-伊红（H&E）和Ki-67染色图像的数据集上进行了全面实验，结果表明所提出的技术优于传统的细胞核分割方案。
