Gustaf Ullman
Division of Scientific Computing
Department of Information Technology
and
Computational and Systems Biology
Department of Cell and Molecular Biology
Uppsala University
The aim is to develop methods for automatic segmentation, tracking and fluorescent spot detection of E coli cells in a microfluidic chamber. The final aim for the methods are to be used as a supporting tool for High throughput biology. The images are challenging due to the moving and dividing cells that needs to be tracked over several generations. Two algorithms have been developed, both based on a set of features and a linear discriminant. These algorithms in combination with an active contour model meets these challenges to a relatively large extent with currently 98% correctly tracked cells. However, due to the large number of time lapse images required to follow cells for several generations, the 2% errors made by the code still implies several days of manual work for each image set.