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.

We introduce a framework for the detection of the brain boundary (arachnoid) within sparse MRI. We use the term sparse to describe volumetric images in which the sampling resolution within the imaging plane is far higher than that of the perpendicular direction. Generic boundary detection schemes do not provide good results for such data. In the scheme we propose, the boundary is extracted using a constrained mesh surface which iteratively approximates a 3D point set consisting of detected boundary points. Boundary detection is based on a database of piecewise constant models, which represent the idealised MR intensity profile of the underlying boundary anatomy. A non-linear matching scheme is introduced to estimate the location of the boundary points using only the intensity data within each image plane. Results are shown for a number of images and are discussed in detail.


Journal article


Med Image Anal

Publication Date





283 - 302


Algorithms, Brain, Computer Simulation, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Theoretical, Reproducibility of Results, Surface Properties