Image analysis for gene expression based phenotype characterization in yeast cells
2016
Image analysis of objects in the microscope scale requires accuracy so that measurements can
be used to differentiate between groups of objects that are being studied. This thesis deals with
measurements in yeast biology that are obtained through microscope images. We study the
algorithms and workflow of image analysis of yeast cells in order to understand and improve
the measurement accuracy. The Saccharomyces cerevisiae cell is widely used as a model
organism in the life sciences. It is essential to study the gene and protein behaviour within
these cells, and consequently making it possible to find treatment and solutions for genetic and
hereditary diseases. This is possible since many processes that occurs at the molecular level in
this organism are similar to those in human cells.
In the research group Imaging and Bioinformatics, we have developed a framework for
analysis of yeast cells. This framework is intended to serve as a support for research in
yeast biology. The framework is integrated in one application and presented via a GUI. The
application integrates modules and algorithms including segmentation, measurement, analysis
and visualization.
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