A non-self-intersecting adaptive deformable surface for complex boundary extraction from volumetric images

Abstract

This paper proposes a non-self-intersecting multiscale deformable surface model with an adaptive remeshing capability. The model is specifically designed to extract the three-dimensional boundaries of topologically simple but geometrically complex anatomical structures, especially those with deep concavities such as the brain, from volumetric medical images. The model successfully addresses three significant problems of conventional deformable models when dealing with such structures-sensitivity to model initialization, difficulties in dealing with severe object concavities, and model self-intersection. The first problem is addressed using a multiscale scheme, which extracts the boundaries of objects in a coarse-to-fine fashion by applying a multiscale deformable surface model to a multiresolution volume image pyramid. The second problem is addressed with adaptive remeshing, which progressively resamples the triangulated deformable surface model both globally and locally, matching its resolution to the levels of the volume image pyramid. Finally, the third problem is solved by including a non-self-intersection force among the customary internal and external forces in a physics-based model formulation. Our deformable surface model is more efficient, much less sensitive to initialization and spurious image features, more proficient in extracting boundary concavities, and not susceptible to self-intersections compared to most other models of its type. This paper presents results of applying our new deformable surface model to the extraction of a spherical surface with concavities from a computer-generated volume image and a brain cortical surface from a real MR volume image.