Random Forests for Multiclass Segmentation using Python API in PerGeos

Simple demonstration of feature computation where the feature vector contains intensity and 2D eigenvalues (X & Y). This example uses random forests implementation from the sklearn package.

IPM_python_tutorial_pergeos

This Xtra includes a PerGeos project and the python script named RandomForests_PerGeos_tutorial__all_slices_advanced.pyscroï and as the name suggests, the script is part of a tutorial to showcase how to use powerful machine learning tools using standard python libraries to achieve multiclass segmentation. Feature descriptor computation includes only two different features, (a) Grayscale Intensity, (b) 2D EigenValues. User can provide sparse labels in order to classify the rest of the volume. Inputs expected are (a) Grayscale volume/image (b) Equal sized (as Grayscale volume) label volume with labels on the 0th slice. This can be extended by choosing any voxels across other slices for the sparse labels but the script currently requires labels only on the 0th slice. Note that while the example project is for PerGeos, this script is compatible for use with Amira-Avizo as well.