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
Digital Image Analysis of Cells: Applications in 2D, 3D and Time
Uppsala University, Interfaculty Units, Centre for Image Analysis.
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Light microscopes are essential research tools in biology and medicine. Cell and tissue staining methods have improved immensely over the years and microscopes are now equipped with digital image acquisition capabilities. The image data produced require development of specialized analysis methods. This thesis presents digital image analysis methods for cell image data in 2D, 3D and time sequences.

Stem cells have the capability to differentiate into specific cell types. The mechanism behind differentiation can be studied by tracking cells over time. This thesis presents a combined segmentation and tracking algorithm for time sequence images of neural stem cells.The method handles splitting and merging of cells and the results are similar to those achieved by manual tracking.

Methods for detecting and localizing signals from fluorescence stained biomolecules are essential when studying how they function and interact. A study of Smad proteins, that serve as transcription factors by forming complexes and enter the cell nucleus, is included in the thesis. Confocal microscopy images of cell nuclei are delineated using gradient information, and Smad complexes are localized using a novel method for 3D signal detection. Thus, the localization of Smad complexes in relation to the nuclear membrane can be analyzed. A detailed comparison between the proposed and previous methods for detection of point-source signals is presented, showing that the proposed method has better resolving power and is more robust to noise.

In this thesis, it is also shown how cell confluence can be measured by classification of wavelet based texture features. Monitoring cell confluence is valuable for optimization of cell culture parameters and cell harvest. The results obtained agree with visual observations and provide an efficient approach to monitor cell confluence and detect necrosis.

Quantitative measurements on cells are important in both cytology and histology. The color provided by Pap (Papanicolaou) staining increases the available image information. The thesis explores different color spaces of Pap smear images from thyroid nodules, with the aim of finding the representation that maximizes detection of malignancies using color information in addition to quantitative morphological parameters.

The presented methods provide useful tools for cell image analysis, but they can of course also be used for other image analysis applications.

Place, publisher, year, edition, pages
Uppsala: Universitetsbiblioteket , 2009. , 57 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 596
Keyword [en]
Digital image analysis, microscopy, fluorescent staining, watershed segmentation, sub-cellular localization, point-like signals, wavelets, cell confluence, cytology, color spaces.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-9541ISBN: 978-91-554-7398-3 (print)OAI: oai:DiVA.org:uu-9541DiVA: diva2:173174
Public defence
2009-02-27, Siegbansalen, Ångstrom Laboratory, Polackbacken, Uppsala, 10:15
Opponent
Supervisors
Available from: 2009-02-05 Created: 2009-02-05Bibliographically approved
List of papers
1. Seeded watersheds for combined segmentation and tracking
Open this publication in new window or tab >>Seeded watersheds for combined segmentation and tracking
2005 (English)In: Image Analysis and Processing – ICIAP 2005, Springer Berlin / Heidelberg , 2005, Vol. 3617, 336-343 p.Chapter in book (Other academic)
Abstract [en]

Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm.

Place, publisher, year, edition, pages
Springer Berlin / Heidelberg, 2005
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 3617/2005
National Category
Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-98013 (URN)10.1007/11553595_41 (DOI)978-3-540-28869-5 (ISBN)
Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2010-06-07Bibliographically approved
2. A detailed analysis of 3D subcellular signal localization
Open this publication in new window or tab >>A detailed analysis of 3D subcellular signal localization
Show others...
2009 (English)In: Cytometry Part A, ISSN 1552-4922, Vol. 75A, no 4, 319-328 p.Article in journal (Refereed) Published
Abstract [en]

Detection and localization of fluorescent signals in relation to other subcellular structures is an important task in various biological studies. Many methods for analysis of fluorescence microscopy image data are limited to 2D. As cells are in fact 3D structures, there is a growing need for robust methods for analysis of 3D data. This article presents an approach for detecting point-like fluorescent signals and analyzing their subnuclear position. Cell nuclei are delineated using marker-controlled (seeded) 3D watershed segmentation. User-defined object and background seeds are given as input, and gradient information defines merging and splitting criteria. Point-like signals are detected using a modified stable wave detector and localized in relation to the nuclear membrane using distance shells. The method was applied to a set of biological data studying the localization of Smad2-Smad4 protein complexes in relation to the nuclear membrane. Smad complexes appear as early as 1 min after stimulation while the highest signal concentration is observed 45 min after stimulation, followed by a concentration decrease. The robust 3D signal detection and concentration measures obtained using the proposed method agree with previous observations while also revealing new information regarding the complex formation.

Keyword
3D image analysis, fluorescence signal segmentation, subcellular positioning, Smad detection
National Category
Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-98014 (URN)10.1002/cyto.a.20663 (DOI)000264513800006 ()
Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2012-05-09Bibliographically approved
3. Robust signal detection in 3D fluorescence microscopy
Open this publication in new window or tab >>Robust signal detection in 3D fluorescence microscopy
2010 (English)In: Cytometry. Part A, ISSN 1552-4922, Vol. 77A, no 1, 86-96 p.Article in journal (Refereed) Published
Abstract [en]

Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point-like signals on image data, often acquired in 3D. Visual detection of such point-like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point-source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point-like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to Visual detection by experts. The proposed method can be used as an efficient point-like signal detection tool for various types of biological 3D image data.

National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:uu:diva-98015 (URN)10.1002/cyto.a.20795 (DOI)000273384700011 ()
Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2011-11-04Bibliographically approved
4. Wavelet based estimation on cell confluence
Open this publication in new window or tab >>Wavelet based estimation on cell confluence
Manuscript (Other academic)
Identifiers
urn:nbn:se:uu:diva-98016 (URN)
Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2010-01-13Bibliographically approved
5. On color spaces for cytology
Open this publication in new window or tab >>On color spaces for cytology
2007 (English)In: SSBA 2007, Symposium i bildanalys i Linköping 14-15 mars 2007, 2007Conference paper, Published paper (Refereed)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-98017 (URN)
Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2010-03-01Bibliographically approved

Open Access in DiVA

fulltext(2949 kB)3850 downloads
File information
File name FULLTEXT01.pdfFile size 2949 kBChecksum SHA-1
63c90e09d7c16aec9079003b6850eeee17a93df0f40ddedaca34b841bd6583a7d2b4e852
Type fulltextMimetype application/pdf
cover(421 kB)120 downloads
File information
File name COVER01.pdfFile size 421 kBChecksum SHA-1
2616c3c9df69291d283e96267c5c91bc14e5447618c099529e95634e40ee483080200357
Type coverMimetype application/pdf
Buy this publication >>

By organisation
Centre for Image Analysis
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 3850 downloads
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

Total: 1725 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