Alsubaie Najah, Trahearn Nicholas, Raza Shan E Ahmed, Snead David, Rajpoot Nasir M
Department of Computer Science, University of Warwick, Coventry, United Kingdom.
Department of Computer Science, Princess Nourah University, Riyadh, Kingdom of Saudi Arabia.
PLoS One. 2017 Jan 11;12(1):e0169875. doi: 10.1371/journal.pone.0169875. eCollection 2017.
Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners.
染色颜色估计是大多数组织学图像处理算法中分析流程的一个重要因素。提供一种可靠且高效的染色颜色反卷积方法对于稳健的算法至关重要。在本文中，我们提出了一种用于组织学图像染色颜色反卷积的新方法。该方法对图像的多分辨率表示进行统计分析，以从相关观测值中分离出独立观测值。然后，我们使用滤波后的不相关数据估计染色混合矩阵。我们进行了大量实验，将所提出的方法与最新的现有方法进行比较，并使用在不同实验室使用不同扫描仪制备的三个不同扫描玻片数据集证明了该方法的稳健性。
