<p>The need for fast multidimensional signal processing arises in many areas. One of the more demanding applications is real time visualization of medical data acquired with e.g. magnetic resonance imaging where large amounts of data can be generated. This data has to be reduced to relevant clinical information, either by image reconstruction and enhancement or automatic feature extraction. Design of fast-acting multidimensional filters has been subject to research during the last three decades. Usually methods for fast filtering are based on applying a sequence of filters of lower dimensionality acquired by e.g. weighted low-rank approximation. Filter networks is a method to design fast multidimensional filters by decomposing multiple filters into simpler filter components in which coefficients are allowed to be sparsely scattered. Up until now, coefficient placement has been done by hand, a procedure which is time-consuming and difficult. The aim of this thesis is to investigate whether genetic algorithms can be used to place coefficients in filter networks. A method is developed and tested on 2-D filters and the resulting filters have lower distortion values while still maintaining the same or lower number of coefficients than filters designed with previously known methods.</p>

The need for fast multidimensional signal processing arises in many areas. One of the more demanding applications is real time visualization of medical data acquired with e.g. magnetic resonance imaging where large amounts of data can be generated. This data has to be reduced to relevant clinical information, either by image reconstruction and enhancement or automatic feature extraction. Design of fast-acting multidimensional filters has been subject to research during the last three decades. Usually methods for fast filtering are based on applying a sequence of filters of lower dimensionality acquired by e.g. weighted low-rank approximation. Filter networks is a method to design fast multidimensional filters by decomposing multiple filters into simpler filter components in which coefficients are allowed to be sparsely scattered. Up until now, coefficient placement has been done by hand, a procedure which is time-consuming and difficult. The aim of this thesis is to investigate whether genetic algorithms can be used to place coefficients in filter networks. A method is developed and tested on 2-D filters and the resulting filters have lower distortion values while still maintaining the same or lower number of coefficients than filters designed with previously known methods.