Denoising multiplexed microscopy images in n-dimensional spectral space

Author:

Harman Rebecca C.1ORCID,Lang Ryan T.1ORCID,Kercher Eric M.1,Leven Paige1,Spring Bryan Q.1

Affiliation:

1. Northeastern University

Abstract

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.

Funder

National Institutes of Health

Richard and Susan Smith Family Foundation

Chan Zuckerberg Initiative

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics,Biotechnology

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