DocumentCode
1083110
Title
Fluid Vector Flow and Applications in Brain Tumor Segmentation
Author
Wang, Tao ; Cheng, Irene ; Basu, Anup
Author_Institution
Dept. of Comput. Sci., Univ. of Alberta, Edmonton, AB
Volume
56
Issue
3
fYear
2009
fDate
3/1/2009 12:00:00 AM
Firstpage
781
Lastpage
789
Abstract
In this paper, we propose a new approach that we call the ldquofluid vector flowrdquo (FVF) active contour model to address problems of insufficient capture range and poor convergence for concavities. With the ability to capture a large range and extract concave shapes, FVF demonstrates improvements over techniques like gradient vector flow, boundary vector flow, and magnetostatic active contour on three sets of experiments: synthetic images, pediatric head MRI images, and brain tumor MRI images from the Internet brain segmentation repository.
Keywords
biomedical MRI; brain; cancer; edge detection; gradient methods; image segmentation; image sequences; medical image processing; paediatrics; tumours; FVF active contour model; Internet brain segmentation repository; boundary vector flow; brain concavity extraction; brain tumor MRI image; brain tumor segmentation; fluid vector flow; gradient vector flow; magnetostatic active contour; pediatric head MRI image; synthetic images; Active contours; Brain modeling; Convergence; Head; Image segmentation; Internet; Level set; Magnetic resonance imaging; Magnetostatics; Neoplasms; Shape; Active contour models; brain tumor; segmentation; snakes; vector flow; Algorithms; Brain; Brain Neoplasms; Computer Simulation; Data Interpretation, Statistical; Head; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Theoretical;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/TBME.2009.2012423
Filename
4760239
Link To Document