14 March 2000 Lessons learned: technology transfer from terrestrial spectroscopy to biomedicine
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Proceedings Volume 3920, Spectral Imaging: Instrumentation, Applications, and Analysis; (2000) https://doi.org/10.1117/12.379583
Event: BiOS 2000 The International Symposium on Biomedical Optics, 2000, San Jose, CA, United States
Abstract
The spectral radiance measured by an imaging spectrometer for a material on the earth's surface has significant dependence on environmental factors such as the illumination environment and the atmospheric conditions. This dependence has limited the success of material identification algorithms that rely on hyperspectral image data without associated ground truth information. An important advantage of hyperspectral data is that the sensor spectral dimensionality typically exceeds the dimensionality of the signature variability for any material of interest. We have shown, for example, that the set of observed 0.4 - 2.5 micrometers spectral radiance vectors for a material on the earth's surface lies in a low-dimensional subspace of the hyperspectral measurement space. This analysis has led to robust algorithms for invariant subpixel image analysis that have been applied to a number of remote sensing applications. Similar computational methods can be applied to biomedical images by introducing variability models for the signatures of interest. We present results for material identification in remote sensing images as well as for the quantification of cell population in 3D brain tissue samples.
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Glenn Healey, Glenn Healey, David Slater, David Slater, } "Lessons learned: technology transfer from terrestrial spectroscopy to biomedicine", Proc. SPIE 3920, Spectral Imaging: Instrumentation, Applications, and Analysis, (14 March 2000); doi: 10.1117/12.379583; https://doi.org/10.1117/12.379583
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