The introduction of novel imaging technologies always carries new challenges regarding the processing of the captured images. 3D Polarized Light Imaging (PLI) is such a new technique. It enables the mapping of single nerve fibers in postmortem human brains in unprecedented detail. Due to the very high resolution at sub-millimeter scale, an immense amount of image data has to be reconstructed three-dimensionally before it can be analyzed. Some of the steps in the reconstruction pipeline require a previous segmentation of the large images. This task of image processing creates black-and-white masks indicating the object and background pixels of the original images. It has turned out that a seeded region growing approach achieves segmentation masks of the desired quality. To be able to process the millions of images acquired with PLI for a single human brain, the region growing has to be parallelized for a supercomputer. However, the choice of the seeds as a preprocessing step of the region growing has to be automated in order to enable a parallel execution. A hybrid parallelization has been applied to the automated seeded region growing to exploit the architecture of a GPU cluster. The hybridity consists of an MPI based parallelization on the CPUs and the execution of some well-chosen, data-parallel subtasks on the GPUs as accelerators. This approach achieves a linear speedup behavior so that the runtime can be reduced to a reasonable amount
