Enhanced diagnostic of skin conditions by polarized laser speckles: Phantom studies and computer modeling

Lioudmila Tchvialeva, Tim K. Lee*, Igor Markhvida, Haishan Zeng, Alexander Doronin, Igor Meglinski

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


The incidence of the skin melanoma, the most commonly fatal form of skin cancer, is increasing faster than any other potentially preventable cancer. Clinical practice is currently hampered by the lack of the ability to rapidly screen the functional and morphological properties of tissues. In our previous study we show that the quantification of scattered laser light polarization provides a useful metrics for diagnostics of the malignant melanoma. In this study we exploit whether the image speckle could improve skin cancer diagnostic in comparison with the previously used free-space speckle. The study includes skin phantom measurements and computer modeling. To characterize the depolarization of light we measure the spatial distribution of speckle patterns and analyse their depolarization ratio taken into account radial symmetry. We examine the dependences of depolarization ratio vs. roughness for phantoms which optical properties are of the order of skin lesions. We demonstrate that the variation in bulk optical properties initiates the assessable changes in the depolarization ratio. We show that image speckle differentiates phantoms significantly better than free-space speckle. The results of experimental measurements are compared with the results of Monte Carlo simulation.

Original languageEnglish
Article number89260X
JournalProceedings of SPIE - International Society for Optical Engineering
Publication statusPublished - 4 Mar 2014
EventPhotonic Therapeutics and Diagnostics X - San Francisco, CA, United States
Duration: 1 Feb 20142 Feb 2014

Bibliographical note

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  • Depolarization ratio
  • Laser speckle imaging
  • Melanoma
  • Monte Carlo modelling
  • Polarization
  • Skin cancer
  • Skin phantom
  • Surface roughness


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