title: Vessel connectivity using Murray's hypothesis.
abstract: We describe a new method for vascular image analysis that incorporates a generic physiological principle to estimate vessel connectivity, which is a key issue in reconstructing complete vascular trees from image data. We follow Murray's hypothesis of the minimum work principle to formulate the problem as an optimization problem. This principle reflects a global property of any vascular network, in contrast to various local geometric properties adopted as constraints previously. We demonstrate the effectiveness of our method using a set of microCT mouse coronary images. It is shown that the performance of our method has a statistically significant improvement over the widely adopted minimum spanning tree methods that rely on local geometric constraints.
authors: Department of Diagnostic Radiology, Yale University, New Haven, CT, USA.
authors: Yifeng
authors: Y
authors: Jiang
authors: Zhen W
authors: ZW
authors: Zhuang
authors: Albert J
authors: AJ
authors: Sinusas
authors: Lawrence H
authors: LH
authors: Staib
authors: Xenophon
authors: X
authors: Papademetris
mesh_terms: D000465:Algorithms; D000818:Animals; D003326:Coronary Circulation; D003331:Coronary Vessels / Q000473:pathology; D003952:Diagnostic Imaging / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D008955:Models, Cardiovascular; D015233:Models, Statistical; D008962:Models, Theoretical; D055114:X-Ray Microtomography / Q000379:methods*