Hello.
Pardon me, I understood your question incorrectly!
You are right in the semse that the _pmap_ does give the final hyperintense regions.
Because of the higher overall brightness of the tissues, the algorithm is picking up most of the white matter as relevant for hyperintense regions of interest. Further, _pamp_ is a probability map i.e, each voxel corresponds to a signal between 0 and 1 representing how strong the corresponding voxel is interms of being classified as a hyperintensity. Hence, we see the very bright regions along with slightly darker ones (in the ex image you have attached here with).

The solution is still to play around with the preprocessing and quantification hyper-paramters. Changing the bias correction level and smoothing factors in the SPM preprocessing pipeline (refer WHyD_preproc.m) would also be relevant.
Further, changing the pmap_cut and clean_th for quantification would help neglect the darker regions on the _pmap_ image (i.e., while computing EV values the low probable darker regions on _pmap_, which ideally popped up because of the issue of overall brightness being high, will not be used). Please refer to the documentation.