We developed RFT to quantify anisotropy as seen from image-based data. RFT enables detailed analysis of large datasets and was implemented in Python and Java (as an ImageJ/Fiji plugin) to provide visualisations of the present structures and angles. We specifically note the ability of RFT to accurately detect multiple overlapping angular features. Thus, RFT is ideal for analysing highly structured samples. RFT allows for accurately extracting angular maps, calculating WOP distributions, and quantifying anisotropic image features. Notably, RFT is also able to dampen the impact of signals with low SNR, thereby allowing to filter data for specific structural features such as masking, angle-amplitude, distance between angles and others making the method highly adaptable to adverse imaging conditions.