ArticleJournal of biomedical optics2024
Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for
Article in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Real-Time Structural Illumination with Hyperspectral Images: A Tunable Projection-Capture Synchronizer for Three-Phase Demodulation on Embedded Heterogeneous Computing Platforms.Sensors (Basel, Switzerland) · 2026Article
- High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- SLIMBRAIN database: A multimodal image database of in vivo human brains for tumour detection.Scientific data · 2025Article
- Special Section Guest Editorial: JBO Special Section on Hyperspectral Imaging.Journal of biomedical optics · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for Aim: Our research compares linescan and snapshot hyperspectral cameras for Approach: We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42 nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information. Results: The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at Conclusion: The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.