Polarimetric Imaging and Computational Techniques for Identification of Malignant Lesions

According to the International Agency for Research on Cancer, cervical cancer is a major cause of death among Moroccan women, with high incidence and mortality rates. Early detection remains essential to increasing patients’ chances of recovery. Our study combines polarized light imaging, digital image correlation (DIC), Gray-Level Co-occurrence Matrix (GLCM) texture analysis, and fractal-based local standard deviation mapping to identify microstructural alterations in cervical tissue. Smear and biopsy samples were collected and anonymized in hospitals in Agadir, Morocco. Our goal is to develop an optical system based on the interaction between polarized light and tissue, as well as a complementary computational framework to distinguish between different types of healthy, precancerous, and cancerous tissue. DIC revealed heterogeneous deformation patterns in cancerous regions, fractal analysis highlighted increased structural complexity, and GLCM features showed higher contrast and entropy in malignant samples. This pilot study introduces a novel approach combining polarimetric imaging and computational analysis, applied to cervical tissue samples from Moroccan women in Africa. Despite the small size of the ex vivo dataset, the results obtained encourage the conduct of larger-scale prospective and in vivo studies.

@article{MEZOUAR2025,
  title     = {Polarimetric Imaging and Computational Techniques for Identification of Malignant Lesions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Mohammed Hachem MEZOUAR and Abdessamad ACHNAOUI and Mohammed TBOUDA and Said CHOUHAM and Said BELKACIM and Mohamed NEJMEDDINE and Driss MGHARAZ},
  doi       = {10.14569/IJACSA.2025.0161286},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161286}
}