One of the most frequent ways to interact with the surrounding environment occurs
as a visual way. Hence imaging is a very common way in order to gain information
and learn from the environment. Particularly in the field of cellular biology, imaging
is applied in order to get an insight into the minute world of cellular complexes. As
a result, in recent years many researches have focused on developing new suitable
image processing approaches which have facilitates the extraction of meaningful
quantitative information from image data sets. In spite of recent progress, but due to
the huge data set of acquired images and the demand for increasing precision, digital
image processing and statistical analysis are gaining more and more importance in
this field.
There are still limitations in bioimaging techniques that are preventing sophisticated
optical methods from reaching their full potential. For instance, in the 3D
Electron Microscopy(3DEM) process nearly all acquired images require manual postprocessing
to enhance the performance, which should be substitute by an automatic
and reliable approach (dealt in Part I). Furthermore, the algorithms to localize individual
fluorophores in 3D super-resolution microscopy data are still in their initial
phase (discussed in Part II). In general, biologists currently lack automated and high
throughput methods for quantitative global analysis of 3D gene structures.
This thesis focuses mainly on microscopy imaging approaches based on Machine
Learning, statistical analysis and image processing in order to cope and improve the
task of quantitative analysis of huge image data. The main task consists of building
a novel paradigm for microscopy imaging processes which is able to work in an
automatic, accurate and reliable way.
The specific contributions of this thesis can be summarized as follows:
• Substitution of the time-consuming, subjective and laborious task of manual
post-picking in Cryo-EM process by a fully automatic particle post-picking
routine based on Machine Learning methods (Part I).
• Quality enhancement of the 3D reconstruction image due to the high performance
of automatically post-picking steps (Part I).
• Developing a full automatic tool for detecting subcellular objects in multichannel
3D Fluorescence images (Part II).
• Extension of known colocalization analysis by using spatial statistics in order
to investigate the surrounding point distribution and enabling to analyze the
colocalization in combination with statistical significance (Part II).
All introduced approaches are implemented and provided as toolboxes which are
free available for research purposes.
| Identifer | oai:union.ndltd.org:MUENCHEN/oai:edoc.ub.uni-muenchen.de:16571 |
| Date | 07 February 2014 |
| Creators | Norousi, Ramin |
| Publisher | Ludwig-Maximilians-Universität München |
| Source Sets | Digitale Hochschulschriften der LMU |
| Detected Language | English |
| Type | Dissertation, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://edoc.ub.uni-muenchen.de/16571/ |
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