title: Homogeneity- and density distance-driven active contours for medical image segmentation.
abstract: In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is necessary because objects in medical images, e.g., bones, are usually highly inhomogeneous while distinct organs should generate distinct image configurations. Compared with the conventional Chan-Vese AC, the proposed model yields similar performance in a set of CT images but performs better in an MRI data set, which is generally in lower contrast.
authors.affiliation: Department of Computer Engineering, Kyung Hee University, Repuplic of Korea.
authors.forename: Phan Tran Ho
authors.initials: PT
authors.lastname: Truc
authors.forename: Tae-Seong
authors.initials: TS
authors.lastname: Kim
authors.forename: Sungyoung
authors.initials: S
authors.lastname: Lee
authors.forename: Young-Koo
authors.initials: YK
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D001842:Bone and Bones / Q000473:pathology; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods