Nanyang Technological University, School of Chemical and Biomedical Engineering, Singapore, 637457, Singapore.
National University Health System, Division of Gastroenterology and Hepatology, Singapore, 119228, Singapore.
J Biophotonics. 2016 Apr;9(4):364-75. doi: 10.1002/jbio.201500006. Epub 2015 Apr 22.
Digital staining based on Mueller matrix measurements and their derivatives was investigated. Mueller matrix imaging was performed at the microscopic level on gastric tissue sections. Full Mueller matrices (4 × 4) were reconstructed using recorded images, followed by the extraction of polarization parameters. The most effective parameters and their combinations were extracted from Mueller matrix elements, principal component scores and polarization parameters respectively to classify samples into three categories - i.e. cancer, dysplasia and intestinal metaplasia/normal glands for various regions of interest sizes. It was observed that two-step classification yielded higher classification accuracy than the traditional one-step classification and that pixel classification based on Mueller matrix elements yielded higher accuracy than that based on polarization parameters and derived principal components. Moreover, Mueller matrix images with a lower spatial resolution generated higher classification accuracy but those with a higher spatial resolution revealed more morphological details.ns. The original stained image (top) and the digital staining image (bottom).
基于 Mueller 矩阵测量及其导数的数字染色进行了研究。在胃组织切片的显微镜水平上进行了 Mueller 矩阵成像。使用记录的图像重建了完整的 Mueller 矩阵（4×4），然后提取了偏振参数。从 Mueller 矩阵元素、主成分得分和偏振参数中分别提取了最有效的参数及其组合，以将样本分为三类——即癌症、发育不良和肠化生/正常腺体，适用于不同的感兴趣区域大小。观察到两步分类比传统的一步分类具有更高的分类准确性，并且基于 Mueller 矩阵元素的像素分类比基于偏振参数和衍生主成分的分类具有更高的准确性。此外，空间分辨率较低的 Mueller 矩阵图像生成更高的分类准确性，但空间分辨率较高的图像揭示了更多的形态细节。
