title: Multiscale active contour model for vessel segmentation.
abstract: This paper presents a novel multiscale active contour model for vessel segmentation. The model is based on accurate analysis of the vessel structure in the image. According to different scale response of the eigenvalues of local second order derivative (Hessian matrix), a new vessel region information function, which shows a valid estimation of the vesselness measure, is defined. We introduce the posteriori probability estimation into the active contours framework and design a new objective function. The defined objective function is minimized using the variational method, and a new region-based external force is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. This active contour model combines the obtained region-based and conventional boundary-based force, which aims at finding more accurate vessel edges even when the vessel branches are low contrast or blurry. Furthermore, the proposed model is implemented by an implicit method of level set framework, the solution of which is steady and suitable for various topology changes. Moreover, two new speed functions for vessel segmentation in the level set method are presented, one for fast marching and the other for a narrow-band algorithm. The vessel segmentation experiments compared with previous geometric active contour models are shown on several medical images. The experimental results demonstrate the performance of our approach.
authors.affiliation: School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, PR China. yugang@mailst.xjtu.edu.cn
authors.forename: G
authors.initials: G
authors.lastname: Yu
authors.forename: P
authors.initials: P
authors.lastname: Li
authors.forename: Y L
authors.initials: YL
authors.lastname: Miao
authors.forename: Z Z
authors.initials: ZZ
authors.lastname: Bian
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012171:Retinal Vessels / Q000033:anatomy & histology