Mualla F, Prümmer M, Hahn D, Hornegger J (2012)
Publication Type: Journal article, Original article
Publication year: 2012
Original Authors: Mualla F., Pruemmer M., Hahn D., Hornegger J.
Publisher: Institute of Physics: Hybrid Open Access
Book Volume: 57
Pages Range: 2555-2573
Journal Issue: 9
DOI: 10.1088/0031-9155/57/9/2555
Vessel diseases are a very common reason for permanent organ damage, disability and death. This fact necessitates further research for extracting meaningful and reliable medical information from the 3D DSA volumes. Murray's law states that at each branch point of a lumen-based system, the sum of the minor branch diameters each raised to the power x, is equal to the main branch diameter raised to the power x. The principle of minimum work and other factors like the vessel type, impose typical values for the junction exponent x. Therefore, deviations from these typical values may signal pathological cases. In this paper, we state the necessary and the sufficient conditions for the existence and the uniqueness of the solution for x. The second contribution is a scale- and orientation- independent set of features for stenosis classification. A support vector machine classifier was trained in the space of these features. Only one branch was misclassified in a cross validation on 23 branches. The two contributions fit into a pipeline for the automatic detection of the cerebral vessel stenoses. © 2012 Institute of Physics and Engineering in Medicine.
APA:
Mualla, F., Prümmer, M., Hahn, D., & Hornegger, J. (2012). Toward automatic detection of vessel stenoses in cerebral 3D DSA volumes. Physics in Medicine and Biology, 57(9), 2555-2573. https://dx.doi.org/10.1088/0031-9155/57/9/2555
MLA:
Mualla, Firas, et al. "Toward automatic detection of vessel stenoses in cerebral 3D DSA volumes." Physics in Medicine and Biology 57.9 (2012): 2555-2573.
BibTeX: Download