Fluid vector flow and applications in brain tumor segmentation.
In this paper, we propose a new approach that we call the "fluid vector flow" (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.
Wang
Tao
T
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, Canada. taowang@cs.ualberta.ca
Cheng
Irene
I
Basu
Anup
A
D000465:Algorithms; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006257:Head; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical