Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The complexity and high morphological variation of cerebral vasculature make comparison and analysis of the vessel patterning difficult and laborious. A framework for automatic labelling of the cerebral vessels in high resolution 3D images has been introduced in the literature that addresses this need. The segmented vasculature is represented as an attributed relational graph. Each vessel segment is an edge in the graph with local attributes such as diameter and length, as well as relational features representing the connectivity of the vessel segments. Each edge in the graph is automatically labelled with an anatomical name through a stochastic relaxation algorithm. In this paper, we compare the performance of four different optimization schemes, including stochastic tunnelling, for automatic labelling. We validated our method on 7 micro-CT images of C57Bl/6J mice with a leave-one-out test. The mean recognition rate of complete cerebrovasculature using stochastic tunnelling is 80% and shows a 2% (>60 vessel segments) improvement compared to simulated annealing optimization. © 2014 IEEE.

Original publication

DOI

10.1109/PRNI.2014.6858519

Type

Conference paper

Publication Date

01/01/2014