A Vascular Network Growth Estimation Algorithm Using Random Graphs.

Autor: Brun, Luc, Vento, Mario, Sung-Hyuk Cha, Gargano, Michael L., Quintas, Louis V., Wahl, Eric M.
Zdroj: Graph-Based Representations in Pattern Recognition; 2005, p45-53, 9p
Abstrakt: Vascular networks develop by way of angiogenesis, a growth process that involves the biological mechanisms of vessel sprouting (budding) and splitting (intussusception). Graph theory is a branch of discrete mathematics that is excellently suited to model vascular networks and to analyze their properties (invariants). A random graph process model can simulate the development of a vascular network that has been modeled using graph theory. The renal glomerulus is one example of such a vascular network. Here the correlation between the invariants of this vascular network modeled as a graph and the mechanisms of the growth of the network are studied. It is proposed that the relative frequencies of sprouting and splitting during the growth of a given renal glomerulus can be estimated by the invariants (root distance, radius, and diameter) of the graph representing the renal glomerulus network. Experimental evidence is given to support this conjecture. [ABSTRACT FROM AUTHOR]
Databáze: Supplemental Index