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Spectral Image Processing with Applications in Biotechnology and Pathology
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 876
Keyword [en]
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: urn:nbn:se:uu:diva-160574ISBN: 978-91-554-8209-1 (print)OAI: oai:DiVA.org:uu-160574DiVA: diva2:451497
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
List of papers
1. Quantification of colocalization and cross-talk based on spectral angles
Open this publication in new window or tab >>Quantification of colocalization and cross-talk based on spectral angles
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
Keyword
Colocalization, cross-talk, fluorescence microscopy, image analysis
National Category
Computer and Information Science
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-111376 (URN)10.1111/j.1365-2818.2009.03170.x (DOI)000266180400011 ()19493110 (PubMedID)
Projects
EU-Strep project ENLIGHT (ENhanced LIGase based Histochemical Techniques)
Available from: 2009-12-15 Created: 2009-12-11 Last updated: 2011-11-23Bibliographically approved
2. Suppression of Autofluorescence based on Fuzzy Classification by Spectral Angles
Open this publication in new window or tab >>Suppression of Autofluorescence based on Fuzzy Classification by Spectral Angles
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, 135-146 p.Conference 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
Keyword
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:nbn:se:uu:diva-111374 (URN)978-0-9563776-0-9 (ISBN)
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: 2011-11-23Bibliographically approved
3. Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy
Open this publication in new window or tab >>Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy
Show others...
2011 (English)In: Cytometry Part A, ISSN 1552-4922, Vol. 79A, no 7, 518-527 p.Article in journal (Refereed) 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.

National Category
Cell and Molecular Biology Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-156962 (URN)10.1002/cyto.a.21087 (DOI)000292947900004 ()
Available from: 2011-08-20 Created: 2011-08-11 Last updated: 2012-06-07Bibliographically approved
4. Blind Color Decomposition of Histological Images
Open this publication in new window or tab >>Blind Color Decomposition of Histological Images
Show others...
2013 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 32, no 6, 983-994 p.Article in journal (Refereed) Published
Abstract [en]

Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy. We extend current linear decomposition methods to include stained tissues where one spectral signature cannot be separated from all combinations of the other tissues' spectral signatures. We demonstrate both qualitatively and quantitatively that our method results in more accurate decompositions than methods based on non-negative matrix factorization and independent component analysis. The result is one density map for each stained tissue type that classifies portions of pixels into the correct stained tissue allowing accurate identification of morphological features that may be linked to cancer.

National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-160312 (URN)10.1109/TMI.2013.2239655 (DOI)000319701800002 ()
Available from: 2011-10-21 Created: 2011-10-21 Last updated: 2017-02-08Bibliographically approved

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