uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
Quantification of colocalization and cross-talk based on 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, Centre for Image Analysis.
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, Centre for Image Analysis.
2009 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 234, no 3, 311-324 p.Article in journal (Refereed) Published
Abstract [en]

Common methods for quantification of colocalization in fluorescence microscopy typically require cross-talk free images or images where cross-talk has been eliminated by image processing, as they are based on intensity thresholding. Quantification of colocalization includes not only calculating a global measure of the degree of colocalization within an image, but also a classification of each image pixel as showing colocalized signals or not. In this paper, we present a novel, automated method for quantification of colocalization and classification of image pixels. The method, referred to as SpecDec, is based on an algorithm for spectral decomposition of multispectral data borrowed from the field of remote sensing. Pixels are classified based on hue rather than intensity. The hue distribution is presented as a histogram created by a series of steps that compensate for the quantization noise always present in digital image data, and classification rules are thereafter based on the shape of the angle histogram. Detection of colocalized signals is thus only dependent on the hue, making it possible to classify also low-intensity objects, and decoupling image segmentation from detection of colocalization. Cross-talk will show up as shifts of the peaks of the histogram, and thus a shift of the classification rules, making the method essentially insensitive to cross-talk. The method can also be used to quantify and compensate for cross-talk, independent of the microscope hardware.

Place, publisher, year, edition, pages
Oxford, UK: Blackwell Publishing , 2009. Vol. 234, no 3, 311-324 p.
Keyword [en]
Colocalization, cross-talk, fluorescence microscopy, image analysis
National Category
Computer and Information Science
Research subject
Computerized Image Analysis
URN: urn:nbn:se:uu:diva-111376DOI: 10.1111/j.1365-2818.2009.03170.xISI: 000266180400011PubMedID: 19493110OAI: oai:DiVA.org:uu-111376DiVA: diva2:281409
EU-Strep project ENLIGHT (ENhanced LIGase based Histochemical Techniques)
Available from: 2009-12-15 Created: 2009-12-11 Last updated: 2011-11-23Bibliographically 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. 63 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 876
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
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)
Available from: 2011-11-11 Created: 2011-10-26 Last updated: 2014-07-21Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMedJoM_page
By organisation
Computerized Image AnalysisCentre for Image Analysis
In the same journal
Journal of Microscopy
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 215 hits
ReferencesLink to record
Permanent link

Direct link