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This paper presents a method to globally segment volumetric images into regions that contain convex or concave (elliptic) iso-surfaces, planar or cylindrical (parabolic) iso-surfaces, and volumetric regions with saddle-like (hyperbolic) iso-surfaces, regardless of the value of the iso-surface level. The proposed scheme relies on a novel approach to globally compute, bound, and analyze the Gaussian and mean curvatures of an entire volumetric data set, using a trivariate B-spline volumetric representation. This scheme derives a new differential scalar field for a given volumetric scalar field, which could easily be adapted to other differential properties. Moreover, this scheme can set the basis for more precise and accurate segmentation of data sets targeting the identification of primitive parts. Since the proposed scheme employs piecewise continuous functions, it is precise and insensitive to aliasing.
Data analysis, Spline, Image segmentation, Image analysis, Image reconstruction, Surface reconstruction, Object recognition, Image databases, Image recognition, segmentation., Index Terms- Gaussian and mean curvature, symbolic computation, global analysis

"Global segmentation and curvature analysis of volumetric data sets using trivariate B-spline functions," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 265-278, 2006.
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