Denoising multiplexed microscopy images in n-dimensional spectral space.

Autor:	Harman RC; Translational Biophotonics Cluster, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.; Department of Physics, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA., Lang RT; Translational Biophotonics Cluster, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.; Department of Physics, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA., Kercher EM; Translational Biophotonics Cluster, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.; Department of Physics, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA., Leven P; Translational Biophotonics Cluster, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.; Department of Physics, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA., Spring BQ; Translational Biophotonics Cluster, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.; Department of Physics, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.; Department of Bioengineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.
Jazyk:	angličtina
Zdroj:	Biomedical optics express [Biomed Opt Express] 2022 Jul 22; Vol. 13 (8), pp. 4298-4309. Date of Electronic Publication: 2022 Jul 22 (Print Publication: 2022).
DOI:	10.1364/BOE.463979
Abstrakt:	Hyperspectral fluorescence microscopy images of biological specimens frequently contain multiple observations of a sparse set of spectral features spread in space with varying intensity. Here, we introduce a spectral vector denoising algorithm that filters out noise without sacrificing spatial information by leveraging redundant observations of spectral signatures. The algorithm applies an n-dimensional Chebyshev or Fourier transform to cluster pixels based on spectral similarity independent of pixel intensity or location, and a denoising convolution filter is then applied in this spectral space. The denoised image may then undergo spectral decomposition analysis with enhanced accuracy. Tests utilizing both simulated and empirical microscopy data indicate that denoising in 3 to 5-dimensional (3D to 5D) spectral spaces decreases unmixing error by up to 70% without degrading spatial resolution. Competing Interests: The authors declare no conflicts of interest. (© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)
Databáze:	MEDLINE
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