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

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Analyzing Tubular Tissue in Histopathological Thin Sections
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
Swedish University of Agricultural Sciences.
Swedish University of Agricultural Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2012 (English)In: 2012 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING TECHNIQUES AND APPLICATIONS (DICTA), IEEE conference proceedings, 2012, 1-6 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE conference proceedings, 2012. 1-6 p.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-188548DOI: 10.1109/DICTA.2012.6411735ISI: 000316318400071OAI: oai:DiVA.org:uu-188548DiVA: diva2:578210
Conference
International Conference on Digital Image Computing Techniques and Applications (DICTA), 3-5 Dec, 2012, Fremantle, AUSTRALIA
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2015-06-03Bibliographically approved
In thesis
1. Computerized Cell and Tissue Analysis
Open this publication in new window or tab >>Computerized Cell and Tissue Analysis
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The latest advances in digital cameras combined with powerful computer software enable us to store high-quality microscopy images of specimen. Studying hundreds of images manually is very time consuming and has the problem of human subjectivity and inconsistency. Quantitative image analysis is an emerging field and has found its way into analysis of microscopy images for clinical and research purposes. When developing a pipeline, it is important that its components are simple enough to be generalized and have predictive value. This thesis addresses the automation of quantitative analysis of tissue in two different fields: pathology and plant biology.

Testicular tissue is a complex structure consisting of seminiferous tubules. The epithelial layer of a seminiferous tubule contains cells that differentiate from primitive germ cells to spermatozoa in a number of steps. These steps are combined in 12 stages in the cycle of the seminiferous epithelium in the mink. The society of toxicological pathology recommends classifying the testicular epithelial into different stages when assessing tissue damage to determine if the dynamics in the spermatogenic cycle have been disturbed. This thesis presents two automated methods for fast and robust segmentation of tubules, and an automated method of staging them. For better accuracy and statistical analysis, we proposed to pool stages into 5 groups. This pooling is suggested based on the morphology of tubules. In the 5 stage case, the overall number of correctly classified tubules is 79.6%.

Contextual information on the localization of fluorescence in microscopy images of plant specimen help us to better understand differentiation and maturation of stem cells into tissues. We propose a pipeline for automated segmentation and classification of the cells in a whole cross-section of Arabidopsis hypocotyl, stem, or root. As proof-of-concept that the classification provides a meaningful basis to group cells for fluorescence characterization, we probed tissues with an antibody specific to xylem vessels in the secondary cell wall. Fluorescence intensity in different classes of cells is measured by the pipeline. The measurement results clearly show that the xylem vessels are the dominant cell type that exhibit a fluorescence signal.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 63 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1262
Keyword
Image processing, Cell, Tissue, Segmentation, Classification, Histology
National Category
Medical Image Processing Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-252425 (URN)978-91-554-9269-4 (ISBN)
Public defence
2015-06-12, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2015-06-03 Created: 2015-05-06 Last updated: 2015-07-07

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Fakhrzadeh, AzadehLuengo Hendriks, Cris L.

Search in DiVA

By author/editor
Fakhrzadeh, AzadehLuengo Hendriks, Cris L.
By organisation
Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer Interaction
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 423 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf