Hello.

features_gen.m script is used to generate the feature_training.mat file.
For each nii image on which training is to be done, we first selected the hyperintense voxels (more on that below). A binary image is then generated where in these hyperintense voxels are +1 and rest all are 0. 
For our case these are the images indexed in line 4, 10-16 of the above script. The binary images correspond to '.._WMH.nii' in the script. For each such training nii image, and for each hyperintense voxel in that image we compute the feature vector (of 2000 dimensions, which is basically the intensity and texture profile in the neighbourhood of the corresponding voxel, from line 20 in the script). The label of this vector will be +1. 
Similarly, non-hyperintense voxels are selected randomly from these same images, and their feature vectors are constructed, which are labelled -1. These are done for all hyperintense (and some non-hyperintense) voxels, across all images, and all such feature vectors are just concatenated (saved as features_training.mat)
The semi-supervised segmentation is done by going through all the training images one-by-one and picking the seeds for hyperintense voxels. Once this is done, a simple random walk (intensity based) is run for all the seeds to generate contiguous hyperintense regions around all the selected seeds. 
Hope these comments are of help.