Classification of tissues is an important problem in biomedicine. An efficient tissue classification protocol allows, for instance, the guided‐recognition of structures through treated images or discriminating between healthy and unhealthy regions (e.g., early detection of cancer). In this framework, we study the potential of some polarimetric metrics, the so‐called depolarization spaces, for the classification of biological tissues. The analysis is performed using 120 biological ex vivo samples of three different tissues types. Based on these data collection, we provide for the first time a comparison between these depolarization spaces, as well as with most commonly used depolarization metrics, in terms of biological samples discrimination. The results illustrate the way to determine the set of depolarization metrics which optimizes tissue classification efficiencies. In that sense, the results show the interest of the method which is general, and which can be applied to study multiple types of biological samples, including of course human tissues. The latter can be useful for instance, to improve and to boost applications related to optical biopsy.

中文翻译：

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用于生物组织分类的去极化度量空间。

组织的分类是生物医学中的重要问题。有效的组织分类规程允许例如通过处理后的图像对结构进行引导识别，或区分健康和不健康区域（例如，癌症的早期检测）。在此框架中，我们研究了一些极化度量（所谓的去极化空间）对生物组织分类的潜力。使用三种不同组织类型的120个生物离体样品进行分析。基于这些数据收集，我们首次提供了在生物样本识别方面这些去极化空间之间以及最常用的去极化指标之间的比较。结果说明了确定去极化指标集的方法，该方法可以优化组织分类效率。从这个意义上讲，结果表明该方法具有普遍意义，可用于研究多种类型的生物样品，当然包括人体组织。后者可用于例如改善和促进与光学活检有关的应用。