Random Forest Classifier

Random Forest Classifier is a method for automatic segmentation of images using train and predict scheme.

The current version of the classifier is based on Random Forest for Membrane Detection by Verena Kaynig and utilize randomforest-matlab by Abhishek Jaiantilal.

The example of use is presented below.

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Dataset and the aim of the segmentation

Here is a dataset (movie) of endoplasmic reticulum taken with wide field light microscopy. The aim is to segment endoplasmic reticulum from the background in the flat areas of the cell. The global black-and-white thresholding will not work in this case due to gradient of the background intensities.

The random forest classifier may be one of the approaches to segment such dataset.

Training the classifier

The first part of the classification is to select areas that belong to the object of interest and background.

By default classifier creates a new directory (RF_Temp) next to the data location that will be used to store images processed for classification and classifier itself. The temporary directory name and the classifier name can be changed in the Temp dir and Classifier filename edit boxes.

Prediction of the whole dataset

When training is done and classificator was saved to a disk go to the 3rd step of the workflow: Predict dataset...

This dialog is allowing to start prediction either for any currently shown slice or for the whole dataset at once. So one can test results of the prediction on any random slice and if the results are acceptable press the Predict dataset button.

When the prediction is finished the results are assigned to the Selection layer. From the Selection layer the results should be transferred to the Model and Mask layer for further polishing and saving to disk.

Wiping the temp directory

During the prediction the classifier creates quite many large size files in the RF_Temp directory. This directory can be deleted by pressing the Wipe Temp dir button or manually using any file explorer.

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