By clicking on it, the whole training process will be interrupted and the plugin reset to the state previous to the training. While training, this button will show the label “STOP”. Notice that all buttons are now enabled, since all their functionalities are possible after training. If the training ends correctly, then the displayed image will be completely segmented and the result will be overlaid with the corresponding class colors. The features will be only calculated the first time we train after starting the plugin or after changing any of the feature options. The feature calculation is done in a completely multi-thread fashion. This step can take some time depending on the size of the images, the number of features and the number of cores of the machine where Fiji is running. The first time this button is pressed, the features of the input image will be extracted and converted to a set of vectors of float values, which is the format the Weka classifiers are expecting. One trace of two classes is the minimum required to start training. This button activates the training process. Training panelĮxample of the aspect of the plugin window after training on a TEM image Train classifier All buttons contain a short explanation of their functionality that is displayed when the cursor lingers over the buttons. On the right side of the image canvas we have a panel with the list of traces for each class and a button to add the current ROI to that specific class. On the left side of the canvas there are two panels of buttons, one for the training and one for the general options. The user can pan, zoom in and out, or scroll between slices (if the input image is a stack) in the main canvas as if it were any other Fiji window. By default, the freehand selection tool (of 1 pixel width) is automatically selected. That includes rectangular, round rectangular, oval, elliptical, brush polygon and freehand selections. The user can add traces to both classes using the whole set of tools for ROI (region of interest) drawing available in Fiji. it will produce binary pixel classification. Both commands will use the same GUI but offer different feature options in their settings.īy default, the plugin starts with two classes, i.e. For 3D features, call the plugin under Plugins › Segmentation › Trainable Weka Segmentation 3D. To use 2D features, you need to select the menu command Plugins › Segmentation › Trainable Weka Segmentation. Trainable Weka Segmentation runs on any 2D or 3D image (grayscale or color). The Graphical User InterfaceĮxample of the first look of the plugin window when using it on a TEM image It provides the framework to use and, more important, compare any available classifier to perform image segmentation based on pixel classification. The main goal of this plugin is to work as a bridge between the Machine Learning and the Image Processing fields.
Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.
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freely availability under the GNU General Public License.
As described on their wikipedia site, the advantages of Weka include: It contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. Weka (Waikato Environment for Knowledge Analysis) can itself be called from the plugin. The Trainable Weka Segmentation is a Fiji plugin that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations. Segmentation: it provides a labeled result based on the training of a chosen classifier. Weka: it makes use of all the powerful tools and classifiers from the latest version of Weka. Trainable: this plugin can be trained to learn from the user input and perform later the same task in unknown (test) data.
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