Analysis and classification of tissue section images using directional fractal dimension features

This paper presents a novel approach to the analysis and classification of tissue section images of human resistance arteries. Real tissue images are modelled using directional fractal dimensions and a multi-layer feedforward neural network is adopted to perform the classification task. This approach has been applied to a large database of images. Simulation results show that modelling cell images with directional fractal dimensions allows the capture of differentiating features not only between normal and abnormal cells but also between the categories within such cells. Directional fractal features entail better discrimination than multi-resolution ones.