Autofluorescence Removal by Non-Negative Matrix Factorization

Abstract

This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is confounded by fluorescence produced by the tissue itself (autofluorescence). Spectral mixing models use mixing coefficients to specify how much fluorescence from each source is present and unmixing algorithms separate the two fluorescent sources. Current spectral unmixing methods for AF removal often require a priori knowledge of mixing coefficients. Those which do not, such as principal component analysis, generate negative mixing coefficients that are not physically meaningful. Non-negative matrix factorization constrains mixing coefficients to be non-negative, and has been used for spectral unmixing, but not AF removal. This paper describes a novel non-negative matrix factorization algorithm which separates fluorescent images into true signal and AF components utilizing an estimate of the dark current. We also present a test-bed, based on fluorescent beads, to compare the performance of different AF removal algorithms. Our algorithm out-performed previous state of the art on validation images.

Optical hyperspectral imaging in microscopy and spectroscopy - a review of data acquisition

“…HSI can overcome these limitations by continuously sampling the emission spectra with a fine spectral resolution. Combined with blind source spectral unmixing algorithms, such as non-negative matrix factorization, HSI can effectively remove the autofluorescence background, resulting in a significant increase in unmixing accuracy and image contrast [82]. …”

Digitally adjusting chromogenic dye proportions in brightfield microscopy images

“…There are many physical problems that the observations are formed by addition of non-negative components. Some of the examples include photon counting processes (Woolfe et al, 2011), gene relations (Kim et al, 2007), text mining (Park & Kim, 2006), and some computer vision applications (Lee & Seung, 1999).…”

Nonnegative matrix factorization using target-to-background contrast for fluorescence unmixing

“…Especially, when excited by an appropriate wavelength of light, some proteins such as collagens and other biological materials such as lipofuscin can also produce autofluorescence (AF) which overlaps with the emission spectra of most fluorophores. Therefore, spectral unmixing (SUM) [4] [5] is required to separate multi-target fluorescence from AF for the qualitative and quantitative analysis in the fluorescence imaging. Currently, AF can be removed by using some acquisition hardware [5] [6] before unmixing fluorescence spectra.…”

“…Therefore, spectral unmixing (SUM) [4] [5] is required to separate multi-target fluorescence from AF for the qualitative and quantitative analysis in the fluorescence imaging. Currently, AF can be removed by using some acquisition hardware [5] [6] before unmixing fluorescence spectra. Because these methods depend on special imaging environments (e.g.…”

“…Therefore, the unsupervised SUM (also called blind source separation, BSS) have been developed to estimate spectra and concentrations simultaneously without a priori knowledge about constituent spectra. Some blind spectral unmixing methods have been already developed to unmix multi-target fluorescence spectra and AF on small-animal examination, such as the NMF with sparseness constraint [5] [7]. NMF was first introduced by Paatero and Tapper in [8] and has gained popularity by the works of Lee and Seung [9].…”