Abstract
An important class of methods for automatic segmentation involves statistically trainable deformable-shape models (SDSMs). The general approach is to apply a SDSM to an image and cause it to undergo a series of deformations converging to a close match between the SDSM and the target anatomic object(s). The deformations are driven by optimizing an objective function with terms for the probability of the geometry of the model, and the goodness of match between the model and the image data. Finding a satisfactory solution for the latter term in general is a challenging research problem.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Radiation Oncology*Biology*Physics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
