ArticleJournal of biomedical optics2021
Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm.
Article in Journal of biomedical optics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Alignment of histological and polarimetric large-scale imaging for brain tissue characterization.Journal of biomedical optics · 2025Article
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- PoLambRimetry: a multispectral polarimetric atlas of lamb brain.Journal of biomedical optics · 2024Article
- Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.Journal of biomedical optics · 2024Article
- Analysis of polarization features of human breast cancer tissue by Mueller matrix visualization.Journal of biomedical optics · 2024Article
- Article
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- Effects of formalin fixation on polarimetric properties of brain tissue: fresh or fixed?Neurophotonics · 2023Article
- AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions.Cancers · 2023Review
- Mueller Matrix Microscopy for In Vivo Scar Tissue Diagnostics and Treatment Evaluation.Sensors (Basel, Switzerland) · 2022Article
- Automatic pseudo-coloring approaches to improve visual perception and contrast in polarimetric images of biological tissues.Scientific reports · 2022Article
- Registration of polarimetric images for in vivo skin diagnostics.Journal of biomedical optics · 2022Article
- Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection.Journal of biomedical optics · 2022Article
- Assessment of tissue pathology using optical polarimetry.Lasers in medical science · 2022Article
- Feasibility of 830 nm laser imaging for vein localization in dark skin tissue-mimicking phantoms.Physical and engineering sciences in medicine · 2022Article
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4 authors.
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Abstract
significanceThe Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application.
aimAn approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. APPROACH: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors.
resultsThe results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier.
conclusionsOverall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.
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