The colorless biopsied tissue samples are usually stained in order to visualize different microscopic structures for diagnostic purposes. But color variations associated with the process of sample preparation, usage of raw materials, diverse staining protocols, and using different slide scanners may adversely influence both visual inspection and computer-aided image analysis. As a result, many methods are proposed for histopathology image stain normalization in recent years. In this study, we introduce a novel approach for stain normalization based on learning a mixture of multivariate skew-normal distributions for stain clustering and parameter estimation alongside a stain transformation technique. The proposed method, labeled “Class-Agnostic Weighted Normalization” (short CLAW normalization), has the ability to normalize a source image by learning the color distribution of both source and target images within an expectation-maximization framework. The novelty of this approach is its flexibility to quantify the underlying both symmetric and nonsymmetric distributions of the different stain components while it is considering the spatial information. The performance of this new stain normalization scheme is tested on several publicly available digital pathology datasets to compare it against state-of-the-art normalization algorithms in terms of ability to preserve the image structure and information. All in all, our proposed method performed superior more consistently in comparison with existing methods in terms of information preservation, visual quality enhancement, and boosting computer-aided diagnosis algorithm performance.

中文翻译：

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使用空间受限的混合模型对组织病理学图像中的染色进行类不可知的加权归一化。

通常将无色的活检组织样品染色，以便可视化不同的显微结构以用于诊断目的。但是，与样品制备，原料使用，不同的染色方案以及使用不同的载玻片扫描仪相关的颜色变化可能会对视觉检查和计算机辅助图像分析产生不利影响。结果，近年来提出了许多用于组织病理学图像染色标准化的方法。在这项研究中，我们介绍了一种基于污渍归一化的新方法，该方法基于学习用于污渍聚类和参数估计的多元偏正态分布的混合以及污渍转换技术。所提出的方法被标记为“类无关加权标准化”（短CLAW标准化），通过在期望最大化框架内学习源图像和目标图像的颜色分布，可以对源图像进行归一化。这种方法的新颖之处在于它可以在考虑空间信息的同时灵活地量化不同污渍成分的潜在对称分布和非对称分布。在几种可公开获得的数字病理数据集上测试了这种新的污渍归一化方案的性能，以在保留图像结构和信息的能力方面将其与最新的归一化算法进行比较。总而言之，在信息保存，视觉质量增强和提高计算机辅助诊断算法性能方面，我们提出的方法在性能上比现有方法更加一致。