Mesoscopic Light Localization and Inverse Participation Ratio Analysis of Tissue Structural Disorder for Optical Cancer Detection
Abstract
We introduce a mesoscopic physics-based framework that transforms conventional transmission optical micrographs into quantitative maps of tissue structural heterogeneity using the Inverse Participation Ratio (IPR). For the first time, to our knowledge, IPR-based light-localization analysis is applied to cancer tissue imaging to quantify nano- to submicron-scale structural alterations through spatial fluctuations in tissue mass density or refractive index. Unlike conventional morphology-based assessment, this physics-driven approach provides objective structural biomarkers from label-free or routinely stained tissue images. The method establishes a scalable, reproducible platform for quantitative computational pathology and enhanced cancer diagnosis by integrating mesoscopic optical physics with standard optical microscopy.
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BibTeX
@misc{maity2026mesoscopic,
title = {{Mesoscopic Light Localization and Inverse Participation Ratio Analysis of Tissue Structural Disorder for Optical Cancer Detection}},
author = {Santanu Maity and Mousa Alrubayan and Prabhakar Pradhan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01960},
eprint = {2609.01960},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01960},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Mesoscopic Light Localization and Inverse Participation Ratio Analysis of Tissue Structural Disorder for Optical Cancer Detection
AU - Santanu Maity
AU - Mousa Alrubayan
AU - Prabhakar Pradhan
PY - 2026
DA - 2026/09/03
PB - arXiv
DO - 10.48550/arxiv.2609.01960
UR - https://arxiv.org/abs/2609.01960
AB - We introduce a mesoscopic physics-based framework that transforms conventional transmission optical micrographs into quantitative maps of tissue structural heterogeneity using the Inverse Participation Ratio (IPR). For the first time, to our knowledge, IPR-based light-localization analysis is applied to cancer tissue imaging to quantify nano- to submicron-scale structural alterations through spatial fluctuations in tissue mass density or refractive index. Unlike conventional morphology-based assessment, this physics-driven approach provides objective structural biomarkers from label-free or routinely stained tissue images. The method establishes a scalable, reproducible platform for quantitative computational pathology and enhanced cancer diagnosis by integrating mesoscopic optical physics with standard optical microscopy.
ER -