Abstract
This paper1 presents a system for classifying cells in human resistance arteries via estimating and analysing fractal dimensions of normal and abnormal cell images. The use of fractal features helps characterise and differentiate between categories of cell images. The classification task is implemented using a multi-layer feedforward neural network, which maps estimated fractal feature patterns onto their underlying cell index. This system has been applied to a large database of images (which were taken from proximal and distal areas of subcutaneous resistance arteries, of patients suffering from critical limb ischaemia, by means of laser scanning confocal microscopy), with the overall classification rate reaching 90.5%.
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© 2000 Springer-Verlag London
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Shang, C., Daly, C., McGrath, J., Barker, J. (2000). Neural Network Based Classification of Cell Images via Estimation of Fractal Dimensions. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_15
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_15
Publisher Name: Springer, London
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