<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>

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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.

Today Image acquisition equipment produces huge amounts of data that needs to be processed. Often the data describes signals with a dimensionality higher then 2, as with ordinary images. This introduce a problem when it comes to process this high dimensional data since ordinary signal processing tools are no longer suitable. New faster and more efficient tools need to be developed to fully exploit the advantages with e. g. a 3D CT-scan. One such tool is filternets, a layered networklike structure, which the signal propagates through. A filternet has three fundamental advantages which will decrease the filtering time. The network structure allows complex filter to be decomposed into simpler ones, intermediate result may be reused and filters may be implemented with very few nonzero coefficients (sparse filters). The aim of this study has been to create an implementation for filternets and optimize it with respect to execution time. Specially the possibility to use filternets that approximates a harmonic filterset for estimating orientation in 3D signals is investigated. Tests show that this method is up to about 30 times faster than a full filterset consisting of dense filters. They also show a slightly larger error in the estimated orientation compared with the dense filters, this error should however not limit the usability of the method.