title: Random walks with adaptive cylinder flux based connectivity for vessel segmentation.
abstract: In this paper, we present a novel graph-based method for segmenting the whole 3D vessel tree structures. Our method exploits a new adaptive cylinder flux (ACF) based connectivity framework, which is formulated based on random walks. To avoid the shrinking problem of elongated structure, all existing graph-based energy optimization methods for vessel segmentation rely on skeleton or ROI extraction. As a result, the performance of these vessel segmentation methods then depends heavily on the skeleton extraction results. In this paper, with the help of ACF based connectivity framework, a global optimal segmentation result can be obtained without extracting skeleton or ROI. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved effectively with the proposed method thanks to the connectivity framework.
authors.affiliation: Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong. nzhu@cse.ust.hk
authors.forename: Ning
authors.initials: N
authors.lastname: Zhu
authors.affiliation: Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong.
authors.forename: Albert C S
authors.initials: AC
authors.lastname: Chung
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity