Abstract
A technique integrating directional fractals and neural networks for modelling and classification of tissue section images of smooth muscle cells in human resistance arteries is presented. The ability of directional fractals in capturing the orientation and roughness degrees of texture images is shown. This ability entails improved results of tissue classification over the use of isotropic multi-resolution fractals.
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