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Conference paper - Peer-reviewed, 2009

Suppression of Autofluorescence based on Fuzzy Classification by Spectral Angles

Gavrilovic Milan, Wählby Carolina


Background fluorescence, also known as autofluorescence, and cross-talk are two problems in fluorescence microscopy that stem from similar phenomena. When biological specimens are imaged, the detected signal often contains contributions from fluorescence originating from sources other than the imaged fluorophore. This fluorescence could either come from the specimen itself (autofluorescence), or from fluorophores with partly overlapping emission spectra (cross-talk). In order to resolve spectral components at least two distinct wavelength intervals have to be imaged. This paper shows how autofluorescence can be presented statistically using a spectral angle histogram. Pixel classification by spectral angles was previously developed for detection and quantification of colocalization. Here we show how the spectral angle histogram can be employed to suppress autofluorescence. First, classical background subtraction (also referred to as linear unmixing) is presented in the form of a fuzzy classification by spectral angles. A modification of the fuzzy classification rules is also presented and we show that sigmoid membership functions lead to better suppression of background and amplification of true signals


autofluorescence; fluorescence microscopy; multispectral image analysis; fuzzy classification; dimensionality reduction

Published in

Book title: Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (OPTIMHisE): A satellite workshop associated with MICCAI
ISBN: 978-0-9563776-0-9


MICCAI 2009, the 12th International Conference on Medical Image Computing and Computer Assisted Intervention

    SLU Authors

    • Gavrilovic, Milan

      • Centre for Image Analysis, Swedish University of Agricultural Sciences
      • Wählby, Carolina

        • Centre for Image Analysis, Swedish University of Agricultural Sciences

      UKÄ Subject classification

      Medical Image Processing

      Permanent link to this page (URI)