Evolutionary Algorithm for Optimizing Parameters of GPGPU-based Image Segmentation

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.