Rodríguez Carla, Van Eeckhout Albert, Ferrer Laia, Garcia-Caurel Enrique, González-Arnay Emilio, Campos Juan, Lizana Angel
Grup d'Òptica, Physics Department, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain.
LPICM, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau 91120, France.
Biomed Opt Express. 2021 Jul 15;12(8):4852-4872. doi: 10.1364/BOE.426387. eCollection 2021 Aug 1.
We highlight the potential of a predictive optical model method for tissue recognition, based on the statistical analysis of different polarimetric indicators that retrieve complete polarimetric information (selective absorption, retardance and depolarization) of samples. The study is conducted on the experimental Mueller matrices of four biological tissues (bone, tendon, muscle and myotendinous junction) measured from a collection of 157 ex-vivo chicken samples. Moreover, we perform several non-parametric data distribution analyses to build a logistic regression-based algorithm capable to recognize, in a single and dynamic measurement, whether a sample corresponds (or not) to one of the four different tissue categories.
我们强调了一种用于组织识别的预测光学模型方法的潜力，该方法基于对不同偏振指标的统计分析，这些指标可获取样本的完整偏振信息（选择性吸收、延迟和去极化）。该研究是基于对157个离体鸡样本集合测量得到的四种生物组织（骨骼、肌腱、肌肉和肌腱连接点）的实验穆勒矩阵进行的。此外，我们进行了多项非参数数据分布分析，以构建一种基于逻辑回归的算法，该算法能够在单次动态测量中识别一个样本是否属于四种不同组织类别之一。
