Automatic screening systems play an increasingly important role in the diagnosis of pathologists. Image measurement and classification are the key techniques of automatic screening systems, which directly determine the performance. The distortion in grey and texture after overlapping nuclei segmentation seriously degrades the DNA content measurement and nuclei classification. In order to solve this problem, this paper presents a new method to reconstruct the pixels in overlapping regions based on the GMM-UBM (Gaussian mixture model–universal background model). In this method, a large amount of data are first used to train a GMM (named UBM). Then, the GMM of each nucleus is derived by maximizing a posteriori adaptation with the UBM and the normal grey value of this nucleus. The grey values are randomly generated by the GMM and filled to the overlapping region, with the offset to fine-tuning the Gaussian components. Finally, the image inpainting algorithm is used to repair the connected region. Experimental results show that this method can effectively recover the nucleus features, such as texture, grey and optical density, and improve the accuracy of nucleus measurement and classification.

中文翻译：

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原子核图像分割中的重叠区域重建

自动筛查系统在病理学家的诊断中发挥着越来越重要的作用。图像测量和分类是自动筛选系统的关键技术，直接决定其性能。重叠核分割后的灰度和纹理失真严重降低了DNA含量测量和核分类。为了解决这个问题，本文提出了一种基于GMM-UBM（高斯混合模型-通用背景模型）重建重叠区域像素的新方法。在该方法中，首先使用大量数据来训练 GMM（命名为 UBM）。然后，通过使用 UBM 和该核的正常灰度值最大化后验适应，推导出每个核的 GMM。灰度值由 GMM 随机生成并填充到重叠区域，偏移量用于微调高斯分量。最后，使用图像修复算法修复连通区域。实验结果表明，该方法可以有效地恢复细胞核的纹理、灰度和光密度等特征，提高细胞核测量和分类的准确性。