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

Volume

2

fYear

2002

fDate

2002

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing. 2002. Proceedings. 2002 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-7622-6

Type

conf

DOI

10.1109/ICIP.2002.1040114

Filename

1040114