Implementation of a Distributed Genetic Algorithm for Parameter Optimization in a Cell Nuclei Detection Project

The processing of microscopic tissue images and especially the detection of cell nuclei is nowadays done more and more using digital imagery and special immunodiagnostic software products. One of the most promising image segmentation methods is region growing, but this algorithm is very sensitive to the appropriate setting of different parameters, and the long runtime due to its high computing demand reduces its practical usability. As a result of our research, we managed to develop a data-parallel region growing algorithm that is two or three times faster than the original sequential version. The paper summarizes our results: the development of an evolution-based algorithm that was used to successfully determine a set of parameters that could be used to achieve significantly better accuracy than the already existing parameters.

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