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Fractal parameters and vascular networks: facts & artifacts

Daniele Mancardi1 email, Gianfranco Varetto2 email, Enrico Bucci2 email, Fabrizio Maniero3 email and Caterina Guiot4 email

1Department of Clinical and Biological Sciences, University of Torino, ASO San Luigi, Regione Gonzole, 10, 10043, Orbassano, Torino, Italy

2Bioindustry Park del Canavese, Colleretto Giacosa, Torino, Italy

3Department of Oncological Sciences and Division of Molecular Angiogenesis, Institute for Cancer Research and Treatment (IRCC), University of Torino Medical School, Strada Provinciale, I-10060 Candiolo,Turin, Italy

4Department of Neuroscience, University of Torino, C. so Raffaello, 30, 10125, Torino, Italy

author email corresponding author email

Theoretical Biology and Medical Modelling 2008, 5:12doi:10.1186/1742-4682-5-12

Published: 17 July 2008

Abstract

Background

Several fractal and non-fractal parameters have been considered for the quantitative assessment of the vascular architecture, using a variety of test specimens and of computational tools. The fractal parameters have the advantage of being scale invariant, i.e. to be independent of the magnification and resolution of the images to be investigated, making easier the comparison among different setups and experiments.

Results

The success of several commercial and/or free codes in computing the fractal parameters has been tested on well known exact models. Based on such a preliminary study, we selected the code Frac-lac in order to analyze images obtained by visualizing the angiogenetic process occurring in chick Chorio Allontoic Membranes (CAM), assumed to be paradigmatic of a realistic 2D vascular network. Among the parameters investigated, the fractal dimension Df proved to be the most robust estimator for CAM vascular networks. Moreover, only Df was able to discriminate between effective and elusive increases in vascularization after drug-induced angiogenic stimulations on CAMs.

Conclusion

The fractal dimension Df is likely to be the most promising tool for monitoring the effectiveness of anti-angiogenic therapies in various clinical contexts.


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