title: Fluid vector flow and applications in brain tumor segmentation.
abstract: 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.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, Canada. taowang@cs.ualberta.ca
authors.forename: Tao
authors.initials: T
authors.lastname: Wang
authors.forename: Irene
authors.initials: I
authors.lastname: Cheng
authors.forename: Anup
authors.initials: A
authors.lastname: Basu
mesh_terms: 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