Zhou Yu, Magee Derek, Treanor Darren, Bulpitt Andrew
School of Computing, University of Leeds, Leeds, UK.
J Pathol Inform. 2013 Mar 30;4(Suppl):S6. doi: 10.4103/2153-3539.109863. Print 2013.
As a critical technique in a digital pathology laboratory, automatic nuclear detection has been investigated for more than one decade. Conventional methods work on the raw images directly whose color/intensity homogeneity within tissue/cell areas are undermined due to artefacts such as uneven staining, making the subsequent binarization process prone to error. This paper concerns detecting cell nuclei automatically from digital pathology images by enhancing the color homogeneity as a pre-processing step.
Unlike previous watershed based algorithms relying on post-processing of the watershed, we present a new method that incorporates the staining information of pathological slides in the analysis. This pre-processing step strengthens the color homogeneity within the nuclear areas as well as the background areas, while keeping the nuclear edges sharp. Proof of convergence for the proposed algorithm is also provided. After pre-processing, Otsu's threshold is applied to binarize the image, which is further segmented via watershed. To keep a proper compromise between removing overlapping and avoiding over-segmentation, a naive Bayes classifier is designed to refine the splits suggested by the watershed segmentation.
The method is validated with 10 sets of 1000 × 1000 pathology images of lymphoma from one digital slide. The mean precision and recall rates are 87% and 91%, corresponding to a mean F-score equal to 89%. Standard deviations for these performance indicators are 5.1%, 1.6% and 3.2% respectively.
The precision/recall performance obtained indicates that the proposed method outperforms several other alternatives. In particular, for nuclear detection, stain guided mean-shift (SGMS) is more effective than the direct application of mean-shift in pre-processing. Our experiments also show that pre-processing the digital pathology images with SGMS gives better results than conventional watershed algorithms. Nevertheless, as only one type of tissue is tested in this paper, a further study is planned to enhance the robustness of the algorithm so that other types of tissues/stains can also be processed reliably.
作为数字病理学实验室中的一项关键技术，自动细胞核检测已经研究了十多年。传统方法直接对原始图像进行处理，由于诸如染色不均等伪像，组织/细胞区域内的颜色/强度均匀性受到破坏，使得后续的二值化过程容易出错。本文关注通过增强颜色均匀性作为预处理步骤，从数字病理学图像中自动检测细胞核。
与以往基于分水岭算法依赖于分水岭后处理不同，我们提出一种新方法，该方法在分析中纳入了病理切片的染色信息。这一预处理步骤增强了细胞核区域以及背景区域内的颜色均匀性，同时保持细胞核边缘清晰。还提供了所提算法的收敛性证明。预处理后，应用大津阈值对图像进行二值化，然后通过分水岭进一步分割。为了在去除重叠和避免过度分割之间保持适当平衡，设计了一个朴素贝叶斯分类器来细化分水岭分割所建议的分割。
该方法用来自一张数字切片的10组1000×1000淋巴瘤病理图像进行了验证。平均精确率和召回率分别为87%和91%，对应的平均F值等于89%。这些性能指标的标准差分别为5.1%、1.6%和3.2%。
所获得的精确率/召回率性能表明，所提方法优于其他几种方法。特别是对于细胞核检测，染色引导的均值漂移（SGMS）在预处理中比直接应用均值漂移更有效。我们的实验还表明，用SGMS对数字病理学图像进行预处理比传统的分水岭算法能得到更好的结果。然而，由于本文仅测试了一种类型的组织，计划进一步研究以提高算法的鲁棒性，以便也能可靠地处理其他类型的组织/染色。
