Mueller matrix imaging of prostate bulk tissues; Polarization parameters as a discriminating benchmark.

The polarimetry imaging technique has provided a powerful tool for discriminating normal from cancerous tissues. In this paper, based on the backscattering Mueller matrix imaging of prostate bulk tissues, (received immediately after surgery without any further processing), we have extracted the characteristic features of the Mueller matrix images. In order to provide a quantitative and more accurate comparison, three different methods have been used; the Mueller matrix polar decomposition (MMPD), the Mueller matrix transformation (MMT) and the frequency distribution histograms (FDHs) and their central moment parameters. Comparing different tissues, the results of our study indicate that these methods provide the indicators for the characteristics of the microstructural features of the tissues. The indicators have the potential to distinguish between cancerous and healthy tissues. Determining the polarimetric characteristics of the tissue immediately after surgery and prior to the pathology, and the potential possibility of this technique to be used in vivo as an optical biopsy technique, can significantly reduce the cost and time of diagnosis of cancer.

Investigation on the potential of Mueller matrix imaging for digital staining.

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).