  The hemocompatibility of blood-contacting medical devices remains one of the
major challenges in biomedical engineering and makes research in the field of
new and improved materials inevitable. However, current in-vitro test and
analysis methods are still lacking standardization and comparability, which
impedes advances in material design. For example, the optical platelet analysis
of material in-vitro hemocompatibility tests is carried out manually or
semi-manually by each research group individually.
  As a step towards standardization, this paper proposes an automation approach
for the optical platelet count and analysis. To this end, fluorescence images
are segmented using Zach's convexification of the multiphase-phase piecewise
constant Mumford--Shah model. The resulting connected components of the
non-background segments then need to be classified as platelet or no platelet.
Therefore, a supervised random forest is applied to feature vectors derived
from the components using features like area, perimeter and circularity. With
an overall high accuracy and low error rates, the random forest achieves
reliable results. This is supported by high areas under the receiver-operator
and the prediction-recall curve, respectively.
  We developed a new method for a fast, user-independent and reproducible
analysis of material hemocompatibility tests, which is therefore a unique and
powerful tool for advances in biomaterial research.
