A framework for anisotropic adaptive filtering and analysis of image sequences and volumes

A framework for analysis and adaptive filtering of time sequences and volume is presented. Time sequences and volumes constitute three-dimensional signal spaces (two spatial dimensions and one time dimension or three spatial dimensions). The signal is convolved with a set of 3D quadrature filters. The filter function is separable in orientation and radius and the uncertainty product of the filters exceeds that of Gabor filters by only 15%. The output from the filters is combined to form a 3D tensor field giving a local description of the neighborhood. To increase robustness the field is convolved with a 3D smoothing filter. This field is used to construct a filter adapting to the local situation. Results showing precise and robust performance using both synthetic and real data are presented