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13 March 2024 A polarization super-pixel framework for extracting polarization features from Mueller matrix images
Jiachen Wan, Haojie Pei, Yue Yao, Tongyu Huang, Hui Ma
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Proceedings Volume PC12845, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2024; PC1284509 (2024) https://doi.org/10.1117/12.3000884
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Mueller matrix describes the polarization properties of the samples comprehensively, characterizing microstructural information at subcellular-level. Mueller matrix microscopy is a promising non-invasive tool for pathological diagnosis, but it can be challenging to extract polarization parameters that correlate with pathological variation. In this study, we propose a polarization super-pixel based polarization feature extraction framework. Polarization super-pixels are able to represent the polarization features of the biological sample in a simple, compact, and comprehensive way, while reducing the data volume drastically. Using various pathological samples including breast cancer, liver cancer, and lung cancer, we show that polarization super-pixel approach greatly increases the efficiency and performance of the downstream supervised and unsupervised learning tasks, for cancerous tissue identification and microstructural composition analysis. We also propose the super-pixel based label spreading method, which iteratively propagates pathologist’s initial manual label of cancerous region to the entire field of view, highlighting the tissues with the same microstructural features.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jiachen Wan, Haojie Pei, Yue Yao, Tongyu Huang, and Hui Ma "A polarization super-pixel framework for extracting polarization features from Mueller matrix images", Proc. SPIE PC12845, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2024, PC1284509 (13 March 2024); https://doi.org/10.1117/12.3000884
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KEYWORDS
Polarization
Mueller matrices
Feature extraction
Tissues
Biological samples
Liver cancer
Lung cancer
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