The use of digital microscopy allows diagnosis through automated quantitative and
qualitative analysis of the digital images. Often to evaluate the samples, the first
step is determining the number and location of cell nuclei. For this purpose, we have
developed a GPGPU based data-parallel region growing algorithm that is equally as
accurate as the already existing sequential versions, but its speed is two or three
times faster (implementing in CUDA environment), but this algorithm is very sensitive
to the appropriate setting of different parameters. Due to the large number of parameters
and due to the big set of possible values setting those parameters manually is a quite
hard task, so we have developed a genetic algorithm to optimize these values. Our
evolution-based algorithm that is described in this paper was used to successfully
determine a set of parameters that compared to the results with the previously known
best set of parameters means a significantly improvement.
