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Suppression of Autofluorescence based on Fuzzy Classification by Spectral Angles
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2009 (English)In: Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (OPTIMHisE): A satellite workshop associated with MICCAI / [ed] Daniel Elson and Nasir Rajpoot, London, 2009, p. 135-146Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
London, 2009. p. 135-146
Keywords [en]
autofluorescence, fluorescence microscopy, multispectral image analysis, fuzzy classification, dimensionality reduction
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-111374ISBN: 978-0-9563776-0-9 (print)OAI: oai:DiVA.org:uu-111374DiVA, id: diva2:281407
Conference
MICCAI 2009, the 12th International Conference on Medical Image Computing and Computer Assisted Intervention
Projects
EU-Strep project ENLIGHT (ENhanced LIGase based Histochemical Techniques)Available from: 2009-12-16 Created: 2009-12-11 Last updated: 2018-01-12Bibliographically approved
In thesis
1. Spectral Image Processing with Applications in Biotechnology and Pathology
Open this publication in new window or tab >>Spectral Image Processing with Applications in Biotechnology and Pathology
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. However, while the focus of image processing often concerns modeling of how images are perceived by humans, the goal of image processing in natural sciences and medicine is the objective analysis. This thesis is focused on color theory that promotes quantitative analysis rather than modeling how images are perceived by humans.

Color and fluorescent dyes are routinely added to biological specimens visualizing features of interest. By applying spectral image processing to histopathology, subjectivity in diagnosis can be minimized, leading to a more objective basis for a course of treatment planning. Also, mathematical models for spectral image processing can be used in biotechnology research increasing accuracy and throughput, and decreasing bias.

This thesis presents a model for spectral image formation that applies to both fluorescence and transmission light microscopy. The inverse model provides estimates of the relative concentration of each individual component in the observed mixture of dyes. Parameter estimation for the model is based on decoupling light intensity and spectral information. This novel spectral decomposition method consists of three steps: (1) photon and semiconductor noise modeling providing smoothing parameters, (2) image data transformation to a chromaticity plane removing  intensity variation while maintaining chromaticity differences, and (3) a piecewise linear decomposition combining advantages of spectral angle mapping and linear decomposition yielding relative dye concentrations.

The methods described herein were used for evaluation of molecular biology techniques as well as for quantification and interpretation of image-based measurements. Examples of successful applications comprise quantification of colocalization, autofluorescence removal, classification of multicolor rolling circle products, and color decomposition of histological images.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. p. 63
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 876
Keywords
color theory, light microscopy, spectral imaging, image analysis, digital image processing, mathematical modeling, estimation, noise models, spectral decomposition, color decomposition, colocalization, cross-talk, autofluorescence, tissue separation, prostate cancer, biomedical applications, molecular biotechnology, histopathology
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-160574 (URN)978-91-554-8209-1 (ISBN)
Public defence
2011-12-02, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2011-11-11 Created: 2011-10-26 Last updated: 2014-07-21Bibliographically approved

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