Spatial-temporal data analysis with non-linear filters: Brain mapping with fMRI data

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model biomedical processes. To map brain functions well-defined sensory activation is offered to a test person and the neuronal response which causes typical changes in the pattern of the blood supply are to be located. The so-called BOLD (Blood Oxygen Level Dependency) effect responses in fMRI are typically characterised by a very low signal to noise ratio, Hence the activation is repeated and the three dimensional signal is gathered during relatively long time ranges (3-5 min). From the noisy and disrupted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.