Autofluorescence Removal by Non-Negative Matrix Factorization
| Autor: | Franco Woolfe, Ali Can, Michael J. Gerdes, Musodiq Bello, Xiaodong Tao |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
business.industry
Image Enhancement Sensitivity and Specificity Computer Graphics and Computer-Aided Design Pattern Recognition Automated Matrix decomposition Non-negative matrix factorization Autofluorescence Microscopy Fluorescence Factorization Subtraction Technique Image Interpretation Computer-Assisted Principal component analysis Source separation Computer vision Artificial intelligence Artifacts business Biological system Spectral method Algorithms Software Mixing (physics) Mathematics |
| Zdroj: | IEEE Transactions on Image Processing. 20:1085-1093 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2010.2079810 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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