uu.seUppsala universitets publikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Blind Color Decomposition of Histological Images
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion.ORCID-id: 0000-0001-7312-8222
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
Vise andre og tillknytning
2013 (engelsk)Inngår i: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 32, nr 6, s. 983-994Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2013. Vol. 32, nr 6, s. 983-994
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-160312DOI: 10.1109/TMI.2013.2239655ISI: 000319701800002OAI: oai:DiVA.org:uu-160312DiVA, id: diva2:450508
Tilgjengelig fra: 2011-10-21 Laget: 2011-10-21 Sist oppdatert: 2018-12-02
Inngår i avhandling
1. Spectral Image Processing with Applications in Biotechnology and Pathology
Åpne denne publikasjonen i ny fane eller vindu >>Spectral Image Processing with Applications in Biotechnology and Pathology
2011 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
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
Emneord
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
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-160574 (URN)978-91-554-8209-1 (ISBN)
Disputas
2011-12-02, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2011-11-11 Laget: 2011-10-26 Sist oppdatert: 2014-07-21bibliografisk kontrollert
2. Automated Tissue Image Analysis Using Pattern Recognition
Åpne denne publikasjonen i ny fane eller vindu >>Automated Tissue Image Analysis Using Pattern Recognition
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy.

In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches.

We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization.

Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis.

Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections.

Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2014. s. 106
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1175
Emneord
tissue image analysis, pattern recognition, digital histopathology, immunohistochemistry, paired antibodies, histological stain evaluation
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-231039 (URN)978-91-554-9028-7 (ISBN)
Disputas
2014-10-20, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-09-29 Laget: 2014-09-02 Sist oppdatert: 2016-04-18bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Azar, JimmyLindblad, JoakimWählby, CarolinaBengtsson, Ewert

Søk i DiVA

Av forfatter/redaktør
Azar, JimmyLindblad, JoakimWählby, CarolinaBengtsson, Ewert
Av organisasjonen
I samme tidsskrift
IEEE Transactions on Medical Imaging

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 767 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf