Bilateral Filtering of fMRI Data

We present a class of adaptive filtering techniques of functional magnetic resonance imaging (fMRI) data related to bilateral filtering. This class of methods average activities in consistent regions rather than regions that maximize correlation with a BOLD model. Similarity measures based on signal similarity and anatomical similarity are discussed and compared experimentally to standard linear low pass filtering. It is demonstrated that adaptive filtering provides improved detection of activated regions.