In this thesis we apply the numerical method of Morphological Geometric Active Contours as proposed by Alvarez, Baumela and Marquez-Neila to microscopic images of plant cells. The goal is to find all plant cells in the images, and then to find their cell walls. This is done in order to calculate certain properties of these cells automatically, such as area, perimeter and ellipticity. As the name suggests, mathematical morphology plays a big part in morphological GAC. This thesis describes the theory behind morphological GAC, mathematical morphology and the implementation in Python of the actual algorithm. We made two adaptations compared to the original model. These are a change in the conditions of the balloon force, and an extra step at the end of each iteration. The change in the balloon force was made to keep edges stronger. The extra step was added to improve smoothness of the contours, since the smoothing parameter had no effect.

When processing a trace DNA sample at the Netherlands Forensic Institute, an STR electropherogram can be created. An analyst uses this electropherogram and analysis software to read out peaks signifying DNA. After analysis, the DNA profile is used in the interpretation process, which can include the comparison to a reference DNA profile of a person of interest. The software that is currently being used for profile analysis is threshold-based and the process includes the intervention of trained analysts. To further automate (allelic) peak identification in STR electropherograms, as well as to increase efficiency and uniformity, neural networks were studied and applied. Previous work by Duncan Taylor and David Powers provided a proof of concept using a simple fully connected neural net. After reviewing literature, the U-net was selected to be used in this thesis. Training U-net on electropherograms proved successful and achieved a 95% accuracy on the per-pixel labels. However, translating the per-pixel output to alleles was more difficult than expected, so an upper bound on the score was calculated. The upper bound got close to an analyst’s performance and demonstrated the potential of this method.