Recently, we developed a label-free method to probe the microstructural information and optical properties of unstained thin tissue slices based on microscopic Mueller matrix imaging technique. In this paper, we take the microscopic Mueller matrix images of human breast ductal carcinoma tissue samples at different pathological stages, and then calculate and analyze their retardance-related Mueller matrix-derived parameters. To reveal the microstructural features more quantitatively and precisely, we propose a new method based on first-order statistical properties of image to transform the 2D images of Mueller matrix parameters into several statistical feature vectors. We evaluate each statistical feature vector by corresponding classification characteristic value extracted from the statistical features of Mueller matrix parameters images of healthy breast duct tissue samples. The experimental results indicate that these statistical feature vectors of Mueller matrix derived parameters may become powerful tools to quantitatively characterize breast ductal carcinoma tissue samples at different pathological stages. It has the potential to facilitate automating the staging process of breast ductal carcinoma tissue, resulting in the improvement of diagnostic efficiency.

中文翻译：

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通过Mueller矩阵成像探测乳腺导管癌组织发育过程中的纤维结构变化

最近，我们开发了一种无标记的方法，以基于显微Mueller矩阵成像技术探测未染色薄组织切片的显微结构信息和光学性质。在本文中，我们拍摄了人类乳腺导管癌组织样品在不同病理阶段的显微Mueller矩阵图像，然后计算和分析了它们与延迟相关的Mueller矩阵派生的参数。为了更定量和精确地揭示微观结构特征，我们提出了一种基于图像一阶统计特性的新方法，将Mueller矩阵参数的2D图像转换为多个统计特征向量。我们通过从健康乳腺导管组织样本的Mueller矩阵参数图像的统计特征中提取相应的分类特征值来评估每个统计特征向量。实验结果表明，穆勒矩阵衍生参数的这些统计特征向量可能成为定量表征不同病理阶段乳腺导管癌组织样本的有力工具。它具有促进乳腺导管癌组织分期过程自动化的潜力，从而提高了诊断效率。实验结果表明，穆勒矩阵衍生参数的这些统计特征向量可能成为定量表征不同病理阶段乳腺导管癌组织样本的有力工具。它具有促进乳腺导管癌组织分期过程自动化的潜力，从而提高了诊断效率。实验结果表明，穆勒矩阵衍生参数的这些统计特征向量可能成为定量表征不同病理阶段乳腺导管癌组织样本的有力工具。它具有促进乳腺导管癌组织分期过程自动化的潜力，从而提高了诊断效率。