Automated coronal hole segmentation from Solar EUV Images using the watershed transform

Region of interest segmentation in solar images is the subject of frequent research in solar physics. This study outlines watershed by immersion segmentation to identify coronal hole areas in solar images acquired using the Extreme UV Imaging Telescope (EIT). Solutions presented here produce highly accurate segmentation results of coronal holes of irregular shape, and what is more, they do so for images representing varied solar activity, recorded in different years and months. In addition, the solutions presented here make all the methods used operate very quickly. These methods include: the preprocessing step before the watershed segmentation, the watershed segmentation itself, and also the postprocessing of solar images after the watershed segmentation. The mean duration of the entire segmentation process of solar images amounts to 342 ms for a single coronal hole, without the parallel implementation of the methods used. The experiments were carried out on a computer with an Intel Core i7 CPU @ 2 GHz and 4 GB RAM. After the seed point is identified inside the coronal hole, the segmentation runs automatically.