Depolarizing spaces for biological tissue classification based on a wavelength combination

Albert Van Eeckhout*, Enric Garcia-Caurel, Razvigor Ossikovski, Angel Lizana, Carla Rodríguez, Emilio González-Arnay, Juan Campos

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


Tissue classification interest is growing due to their numerous applications in the biomedical framework. This increasing interest leads to the rise of alternative techniques in order to enhance the classification accuracy. Polarimetric methods, and in particular, the study of depolarization properties of samples, appear to be an alternative and complementary technique that helps to discriminate between different organic tissues. In fact, depolarization is directly related with the scattering processes produced in biological tissues, providing different responses when studied in different fibre based structures. In the current state of art, there exists different metrics devised to study depolarizing properties of samples and it is not clear which method is the most suitable for tissue classification. By using some of those polarimetric metrics, a compendium of depolarizing spaces can be constructed, composed of three observables each. Such depolarizing spaces stand out over the other existing polarimetric indicators as they allow synthetizing the depolarization content of samples, to provide further analysis of depolarizers. In this work, we compare the effectivity of different depolarizing spaces in order to determine the best space for tissue classification. This is done by using a collection of chicken thigh tissues. In particular, we have measured the Mueller matrix of 120 samples spread around three chicken thigh tissues: tendon, muscle and myotendinous junction. Different tissues have been imaged at different wavelengths (470 nm, 530 nm and 625 nm) to obtain more information and to amplifying the classification sensitivity with this extra channel (wavelength). Finally, we conclude the work by showing the depolarization space providing the best results in terms of tissue classification.

Original languageEnglish
Article number113631G
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - Apr 2020


  • Biological tissue
  • Biomedical
  • Depolarization
  • Imaging
  • Mueller matrix
  • Polarimetry
  • Tissue classification


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