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Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylära verktyg. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylära verktyg. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylära verktyg. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
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2011 (Engelska)Ingår i: Cytometry Part A, ISSN 1552-4922, Vol. 79A, nr 7, s. 518-527Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Specific single-molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single-molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost-efficient wide-field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point-like signals in dual-channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single-molecule detection events in situ.

Ort, förlag, år, upplaga, sidor
2011. Vol. 79A, nr 7, s. 518-527
Nationell ämneskategori
Cell- och molekylärbiologi Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:uu:diva-156962DOI: 10.1002/cyto.a.21087ISI: 000292947900004OAI: oai:DiVA.org:uu-156962DiVA, id: diva2:435817
Tillgänglig från: 2011-08-20 Skapad: 2011-08-11 Senast uppdaterad: 2022-01-28Bibliografiskt granskad
Ingår i avhandling
1. Spectral Image Processing with Applications in Biotechnology and Pathology
Öppna denna publikation i ny flik eller fönster >>Spectral Image Processing with Applications in Biotechnology and Pathology
2011 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2011. s. 63
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 876
Nyckelord
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
Nationell ämneskategori
Medicinsk bildbehandling
Forskningsämne
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-160574 (URN)978-91-554-8209-1 (ISBN)
Disputation
2011-12-02, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2011-11-11 Skapad: 2011-10-26 Senast uppdaterad: 2014-07-21Bibliografiskt granskad

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Gavrilovic, MilanSöderberg, OlaWählby, Carolina

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Gavrilovic, MilanWeibrecht, IreneSöderberg, OlaWählby, Carolina
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Centrum för bildanalysBildanalys och människa-datorinteraktionMolekylära verktygScience for Life Laboratory, SciLifeLab
Cell- och molekylärbiologiDatorseende och robotik (autonoma system)

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