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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mandelbrot BB. The fractal geometry of nature. San. Francisco: Freeman, 1982
Keller J, Chen S, Crownover R. Texture description and segmentation through fractal geometry. Comp. Vision Graph, and Image Proc. 1989; 45:150–166
Steve D, Hall P. Fractal analysis of surface roughness by using spatial data. J. R. Statist. Soc.B 1999; 61:1–27
Wu CM, Chen YC. Texture features for classification of ultrasonic liver images. IEEE Trans. Medical Imaging 1992; 11:141–152
Bishop CM. Neural networks for pattern recognition. Oxford University Press, Oxford, 1995
Jennings M, Graham J. A neural network approach to automatic chromosome classification. Phs. Med. Boil. 1993; 38:959–970
Shang C, Brown KE. Principal feature based texture classification with neural networks. Pattern Recognition 1994; 27:675–687
Rumelhant DE, Hinton GE, Williams RJ. Learning internal representations by error propagating. In: Parallel Distributed Processing. Rumelhant E, McClelland JL. (Eds.), MIT Press, 1986
Mulvang MJ. Vascular remodelling of resistance vessels: can we define this? J. Cardiovascular Research 1999; 41:9–13
Daly CJ, Gordon JF, McGrath JC. The use of fluorescent nuclear dyes for the study of blood vessel structure and function: novel applications of existing techniques. J. Cardiovascular Research 1992; 29:41–48
Arribas SM, Gordon JF, Daly CJ, Dominiczak AF, McGrath JC. Confocal microscopic characterisation of a lesion in an acerebral vessel of the stroke-prone spontaneously hypertensive rat. Stroke 1996; 27:1118–1123
Duda RO, Hart PE. Pattern classification and scene analysis. Jone Wiley and Sons, New York, 1993
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag London
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0513-8_15
Publisher Name: Springer, London
Print ISBN: 978-1-85233-289-1
Online ISBN: 978-1-4471-0513-8
eBook Packages: Springer Book Archive