Accurate lung segmentation from high resolution CT images is a challenging task due to various detail tracheal
structures, missing boundary segments and complex lung anatomy. One popular method is based on gray-level threshold,
however its results are usually rough. A united geometric active contours model based on level set is proposed for lung
segmentation in this paper. Particularly, this method combines local boundary information and region statistical-based
model synchronously: 1) Boundary term ensures the integrality of lung tissue.2) Region term makes the level set
function evolve with global characteristic and independent on initial settings. A penalizing energy term is introduced into
the model, which forces the level set function evolving without re-initialization. The method is found to be much more
efficient in lung segmentation than other methods that are only based on boundary or region. Results are shown by 3D
lung surface reconstruction, which indicates that the method will play an important role in the design of computer-aided
diagnostic (CAD) system.
A protection scheme that chooses protection routes in advance in All-Optical Mesh network is proposed in this paper. Two rules, minimum relativity among routes and minimum the number of hops, are given and analyzed in detail. In order to perform protection quickly and correctly, the compromise between two principles must be considered when choosing protection routes. The protection method that appointing ring networks in mesh networks is proposed too. In addition, some key technologies such as avoiding oscillation, line protection and misconnect squelched are also proposed in this paper.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.