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This article proposes a Rapid Prototyping (RP) procedure for manufacturing models of organs and tissues from medical imaging data. This RP procedure constitutes four stages: The key task of the first stage is to build the preliminary geometrical model of the target structure. To accomplish this goal, we extract the iso-surface of the target structure at first. Then we discretize the iso-surface into voxels and perform morphological operations upon the iso-surface to remove dangling parts and to fill small holes and gaps. At the second stage, a distance field is generated in the volume space, which encloses the iso-surface. Then a Laplacian filtering is performed to alleviate sharp geometrical features in the distance field. At the third stage, we extract a new iso-surface from the distance field to form the outer surface of the target structure. This iso-surface is the offset surface of the preliminary iso-surface. It forms a hollow solid model of the target object. At the final stage, this solid model is converted into G-codes and is manufactured by using a 3D printer.

This work handles fuzzy utility sequential pattern mining, which applies the fuzzy set concept to find interesting high utility sequential patterns with linguistic meaning. In particular, a new fuzzy utility function is developed to evaluate the linguistic benefit of a pattern in a sequence, and an effective approach is developed to find high fuzzy utility sequential patterns from a quantitative sequence database. The experimental results show the proposed approach has good performance under various parameter settings.

We develop a novel stabilized thresholding methodology for image denoising. Main features of this new proposal include a new thresholding rule that repairs the known drawbacks of both hard and soft thresholding, and the use of the generalized Stein's unbiased risk estimation technique for automatic parameter selection. Another advantage of our approach is that it can be applied to different types of noise distributions. In this paper we consider both the Gaussian and mixture of Gaussians, where the latter case can be used to model data with outliers. Practical performance of our proposal is evaluated via numerical experiments.