IEEE J Biomed Health Inform. 2017 May;21(3):826-837. doi: 10.1109/JBHI.2016.2544245. Epub 2016 Mar 21.
Efficient and accurate detection of cell nuclei is an important step toward automatic analysis in histopathology. In this work, we present an automatic technique based on generalized Laplacian of Gaussian (gLoG) filter for nuclei detection in digitized histological images. The proposed technique first generates a bank of gLoG kernels with different scales and orientations and then performs convolution between directional gLoG kernels and the candidate image to obtain a set of response maps. The local maxima of response maps are detected and clustered into different groups by mean-shift algorithm based on their geometrical closeness. The point which has the maximum response in each group is finally selected as the nucleus seed. Experimental results on two datasets show that the proposed technique provides a superior performance in nuclei detection compared to existing techniques.
高效准确地检测细胞核是实现组织病理学自动分析的重要步骤。在这项工作中，我们提出了一种基于广义拉普拉斯高斯（gLoG）滤波器的自动细胞核检测技术，用于数字化组织学图像。该技术首先生成一组具有不同尺度和方向的 gLoG 核，然后在候选图像上进行方向 gLoG 核的卷积，以获得一组响应图。通过基于几何接近度的均值漂移算法检测响应图的局部极大值，并将其聚类成不同的组。最后，在每个组中选择具有最大响应的点作为细胞核种子。在两个数据集上的实验结果表明，与现有技术相比，该技术在细胞核检测方面具有更好的性能。
