![]() In our example, we found that a value of about 600 gives reasonable results. With the “smoothness” slider we can adjust the smoothness of the segmentation to avoid some small extrema to corrupt our segmentation. Now we start the Graph Cut plugin on the probability image. Instead of using the classifier directly for the segmentation, however, we create a probability image: We train a classifier for the mitochondria and everything else. For that, we can use the Trainable Weka Segmentation plugin. TutorialĪssume we want to segment the following image into foreground/background, such that the foreground is the mitochondria and the background everything else:įirst, we create a probability image that reflects the per-pixel probability of belonging to the foreground. A value of zero corresponds to thresholding the input image. The higher this value, the less label changes you will have, thus the segmentation gets smoother. Use it to adjust the penalty for label changes in the segmentation. The only available setting so far is the “smoothness” value. The following configuration has been tested in a machine with 8 CPU cores and 16 Gb of RAM, running Ubuntu 8.04 'Hardy', with a 1.6. Running fiji for heavy-duty, memory-intensive, high-performance TrakEM2 tasks. The plugin can be found under Plugins › Segmentation › Graph Cut. Manual TrakEM2 tutorials with video tutorials. Yuri Boykov and Vladimir Kolmogorov, An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision., In IEEE Transactions on Pattern Analysis and Machine Intelligence, September 2004. ThunderSTORM is an open-source, interactive, and modular plug-in for ImageJ designed for automated processing, analysis, and visualization of data acquired by single molecule localization microscopy methods such as PALM and STORM. ![]() If you intend to use it for a publication, please cite: This plugin is based on a reimplementation of Kolmogorov’s maxflow v3.01 library, which was written in C++. Via a single parameter you can adjust the smoothness of the segmentation. As input, you have to provide a gray-scale image that represents the pixel affinities for belonging to the foreground. The Graph Cut plugin provides a way to obtain a globally smooth binary segmentation. If you’d like to help, check out the how to help guide! The content of this page has not been vetted since shifting away from MediaWiki.
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