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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.

Type

Journal article

Journal

Med Image Anal

Publication Date

09/2000

Volume

4

Pages

283 - 302

Keywords

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