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Biological image analysis of model organisms

Daniel Ochoa Donoso UGent (2011)
abstract
Model organisms are perhaps the most important experimental subjects in biology nowadays. These specimens have characteristics such as short life cycles and compact genome sequences that make them suitable for a number of experimental techniques at different biological levels. They are widely used to explore biology fundamentals and bioengineering products. Due to its importance, there is a need for methods to process the ever increasing amount of biological image data related to model organism research. In this PhD microscopic images of two model organisms, the C. elegans nematode and the A. thaliana plant, are studied. Biological images are inherently difficult to process due to noise, blur, clutter and optical effects. The main problem when sample measurements must be extracted either automatically or semi-automatically is specimen detection. The focus in this PhD has been the usability of several image processing tools that incorporate prior knowledge into the image processing chain. The first problem addressed is the detection of elongated specimens laying in isolation. For this, differential geometry and scale space principles are proposed to describe and detect linear objects. Differential geometry allows us to characterize the shape of an image surface in terms of image derivatives. Scale space provides a mathematical framework to describe image features according to the scale of the observation. As a result a set of features is proposed to detect individual specimens. Another part of this PhD research concerns with the extraction of statistics from adult C. elegans nematode populations imaged at low magnifications. The active contour framework is used to extract shape evidence and use it for detection. After convergence contour energies can be related to image and geometrical properties of the segmented object. We propose a detection technique that exploits this characteristic to extract a sample of individuals located in clusters. Population measurements are computed from these samples and compared to manual measurements. Finally, we concentrate on the problem of segmenting cells of A. thaliana epidermal tissue. These samples are imaged using Differential Interference Contrast technique. To generate sufficient image data, we utilize a revolving stage and different focus settings. A technique to enhance the cellular wall is proposed. It is demonstrated experimentally that the enhanced image can be effectively processed using a well-established segmentation algorithm.
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author
promoter
UGent, Boris Vintimilla and UGent
organization
alternative title
Biologische beeldanalyse van modelorganismen
year
type
dissertation (monograph)
subject
keyword
Model Organism, Computer vision
pages
XIX, 170 pages
publisher
Ghent University. Faculty of Engineering and Architecture
place of publication
Ghent, Belgium
defense location
Gent : Faculteit Ingenieurswetenschappen (Jozef Plateaustraat 22, auditorium E)
defense date
2011-09-21 16:00
ISBN
9789085784463
language
English
UGent publication?
yes
classification
D1
copyright statement
I have retained and own the full copyright for this publication
id
1919006
handle
http://hdl.handle.net/1854/LU-1919006
date created
2011-09-29 19:04:00
date last changed
2011-10-03 09:10:02
@phdthesis{1919006,
  abstract     = {Model organisms are perhaps the most important experimental subjects in biology nowadays. These specimens have characteristics such as short life cycles and compact genome sequences that make them suitable for a number of experimental techniques at different biological levels. They are widely used to explore biology fundamentals and bioengineering products. Due to its importance, there is a need for methods to process the ever increasing amount of biological image data related to model organism research. 
In this PhD microscopic images of two model organisms, the C. elegans nematode and the A. thaliana plant, are studied. Biological images are inherently difficult to process due to noise, blur, clutter and optical effects. The main problem when sample measurements must be extracted either automatically or semi-automatically is specimen detection.  The focus in this PhD has been the usability of several image processing tools that incorporate prior knowledge into the image processing chain. 
The first problem addressed is the detection of elongated specimens laying in isolation. For this,  differential geometry and scale space principles are proposed to describe and detect linear objects. Differential geometry allows us to characterize the shape of an image surface in terms of image derivatives. Scale space provides a mathematical framework to describe image features according to the scale of the observation. As a result a set of features is proposed to detect  individual specimens.
Another part of this PhD research concerns with the extraction of statistics from adult C. elegans nematode populations imaged at low magnifications. The active contour framework is used to extract shape evidence and use it for detection.
After convergence contour energies can be related to image and geometrical properties of the segmented object. We propose a detection technique that exploits this characteristic to extract a sample of individuals located in clusters. Population measurements are computed from these samples and compared to manual measurements.
Finally, we concentrate on the problem of segmenting cells of A. thaliana epidermal tissue. These samples are imaged using Differential Interference Contrast technique.  To generate sufficient image data, we utilize a revolving stage and different focus settings. A technique to enhance the cellular wall is proposed. It is demonstrated experimentally that the enhanced image can be effectively processed using a well-established segmentation algorithm.},
  author       = {Ochoa Donoso, Daniel},
  isbn         = {9789085784463},
  keyword      = {Model Organism,Computer vision},
  language     = {eng},
  pages        = {XIX, 170},
  publisher    = {Ghent University. Faculty of Engineering and Architecture},
  school       = {Ghent University},
  title        = {Biological image analysis of model organisms},
  year         = {2011},
}

Chicago
Ochoa Donoso, Daniel. 2011. “Biological Image Analysis of Model Organisms”. Ghent, Belgium: Ghent University. Faculty of Engineering and Architecture.
APA
Ochoa Donoso, D. (2011). Biological image analysis of model organisms. Ghent University. Faculty of Engineering and Architecture, Ghent, Belgium.
Vancouver
1.
Ochoa Donoso D. Biological image analysis of model organisms. [Ghent, Belgium]: Ghent University. Faculty of Engineering and Architecture; 2011.
MLA
Ochoa Donoso, Daniel. “Biological Image Analysis of Model Organisms.” 2011 : n. pag. Print.