Title :
Analysis of blood vessel topology by cubical homology
Author :
Niethammer, Marc ; Stein, A.N. ; Kalies, W.D. ; Pilarczyk, P. ; Mischaikow, K. ; Tannenbaum, A.
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
We segment and topologically classify brain vessel data obtained from magnetic resonance angiography (MRA). The segmentation is done adaptively and the classification by means of cubical homology, i.e. the computation of homology groups. In this way the number of connected components; (measured by H0), the tunnels (given by H1) and the voids (given by H2) are determined, resulting in a topological characterization of the blood vessels.
Keywords :
adaptive signal processing; biomedical MRI; blood vessels; image classification; image segmentation; medical image processing; adaptive thresholding algorithm; blood vessel topology; brain vessel data classification; connected components; cubical homology; homology groups computation; magnetic resonance angiography; topological characterization; tunnels; voids; Biomedical imaging; Blood vessels; Data engineering; Filters; Gaussian distribution; Image processing; Image segmentation; Magnetic resonance; Mathematics; Topology;
Conference_Titel :
Image Processing. 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7622-6
DOI :
10.1109/ICIP.2002.1040114
