Indices of polarimetric purity: application in biological tissues

Abstract

Complete characterization of biological samples is of potential interest in different industrial and research areas, as for instance, in biomedical applications, for the recognition of organic structures or for the early detection of some diseases. During the last decades, polarimetric methods are experiencing an increase of attention in the study of biomedical tissues, and they are nowadays used in such framework to provide qualitative (polarimetric imaging) and quantitative (data processing) information for the studied samples. Polarimetric methods are based on the analysis of polarization modifications produced by light-matter interactions which can be triggered by a number of complex internal processes but can be roughly understood as the result of the combination of three pure polarimetric features of the sample: its diattenuation, retardance and depolarization. For the analysis of the depolarization content, we propose the use of the Indices of Polarimetric Purity (IPP) to describe the sample behavior. Related with the randomness of the scattering processes, IPPs provide more information of depolarizing systems than the widely used depolarization index (𝑃 ∆ ), which further synthetize the depolarization content of samples. Moreover, certain combinations of IPP parameters leads to 𝑃 ∆ . As a result, IPPs allow the revelation of some structures from tissue samples hidden in regular intensity images of even in the 𝑃 ∆ channel, leading to better tissue classification results. In this work, we present different applications of IPPs in biomedical tissue that show its potential, which are not restricted to the biomedical framework as relevant results in plants characterization are also presented.

Optimizing the classification of biological tissues using machine learning models based on polarized data

“…Next, we also consider a set of three depolarizing metrics, the indices of polarimetric purity (IPPs), which have proved to be specially interesting to analyze biological tissues [18][19][20] and that can be naturally deduced from the Cloude's decomposition [35]. From this approach, the IPPs, labeled P 1 , P 2 and P 3 , are directly retrieved from the eigenvalues, λ i (being i = 0,…, 3), of the Mueller-associated covariance matrix H(M) [55,56] as follows:…”

“…Next, we also consider a set of three depolarizing metrics, the indices of polarimetric purity (IPPs), which have proved to be specially interesting to analyze biological tissues [18–20] and that can be naturally deduced from the Cloude's decomposition [35]. From this approach, the IPPs, labeled P 1 , P 2 and P 3 , are directly retrieved from the eigenvalues, λ i (being i = 0,…, 3), of the Mueller‐associated covariance matrix H (M) [55, 56] as follows: where the IPP values are restricted to 0 ≤ P 1 ≤ P 2 ≤ P 3 ≤ 1, the covariance matrix eigenvalues are taken in the decreasing order: λ 0 ≥ λ 1 ≥ λ 2 ≥ λ 3 .…”

“…In the case of biological applications, polarimetric methods have proved to be suitable tools to unveil biological structures not visible by using other standard optical techniques. In addition, they are also useful to enhance the image contrast between different biological structures [18–21]. This capability of polarimetric solutions to discriminate between organic tissues, is nowadays used to aid on the early detection of human diseases such as different types of cancer (skin [8], breast [5, 6] or colon [7, 22], among others [23, 24]).…”

Study of the suitability of polarimetric metrics to implement tissue classification models based on artificial intelligence

“…In this sense, polarimetric techniques provide information of biological structures not visible by using other standard optical techniques. Moreover, they can also be used to enhance the image contrast between different biological structures 8,9 . In the last decade, polarimetric information of biological samples has shown its potential to discriminate between organic tissues and it is nowadays used to aid on the early detection of human diseases such as different types of cancer (e.g.…”