DocumentCode
2109373
Title
Supporting reconstruction of the blood vessel network using graph theory: An abstraction method
Author
Bossard, Antoine ; Kato, Toshihiko ; Masuda, Kohji
Author_Institution
Grad. Sch. of Bio-Applic. & Syst. Eng., Tokyo Univ. of Agric. & Technol., Koganei, Japan
fYear
2012
fDate
Aug. 28 2012-Sept. 1 2012
Firstpage
5470
Lastpage
5473
Abstract
The blood vessel network (BVN) has a complex structure. As this structure is unique for each individual, it is not possible to establish a general model for the BVN. However, many medical applications do rely on this structure. For example, a drug delivery system would be greatly improved if it could control the drug flow towards destination. To address this BVN structure issue, several reconstruction methods have been introduced. In this paper, we describe an abstraction method supporting BVN reconstruction by using graph theory. Starting from an original BVN reconstruction, we define the so-called induced graph of that reconstruction, allowing for an efficient analysis. By applying this method, we were able to improve an original BVN reconstruction of a human kidney by pointing out probable errors inside that original reconstruction.
Keywords
biomedical optical imaging; blood flow measurement; blood vessels; drug delivery systems; drugs; graph theory; image reconstruction; kidney; medical image processing; optical tomography; abstraction method; blood vessel network reconstruction; drug delivery system; drug flow; graph theory; human kidney; induced graph; medical applications; optical tomography; probable errors; Bifurcation; Biomedical imaging; Blood; Doppler effect; Image edge detection; Image reconstruction; Blood Vessels; Computer Graphics; Humans; Kidney;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location
San Diego, CA
ISSN
1557-170X
Print_ISBN
978-1-4244-4119-8
Electronic_ISBN
1557-170X
Type
conf
DOI
10.1109/EMBC.2012.6347232
Filename
6347232
Link To Document