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In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.

In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.

In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.

In this chapter we have shown how appropriateness measures can be extended to the case where the elements of Ω are described in terms of multiple attributes. The resulting calculus is based on extended definitions for λ-sets and mass assignments, referred to as mass relations in the multi-dimensional case. We have argued for the conditional independence assumption that once the value of a attribute is known, then Your decision regarding the appropriateness of labels for describing that attribute does not dependent on the values of other attributes. This assumption helps to maintain computational tractability and also leads to a number of intuitive properties as identified in theorems 72 and 73.

In addition, to the multi-attribute case we have also extended appropriateness measures and mass assignments to model the description of a set of objects or instances. Given a set of instances Ω and a subset of labels we can infer a mass assignment () quantifying the likelihood of selecting an element of for which corresponds to the set labels You judge as appropriate to describe the element. This mass assignment then defines a measure of appropriateness for an expression as a description of . In the case that the elements of have multiple attributes then the estimation of a mass relation can be problematic due to the curse of dimensionality. This can in part be overcome by a semi-independence assumption whereby the attributes are partitioned into dependent groups. Mass relations are then determined from for the joint spaces identified by each attribute grouping and independence is assumed between groups.

In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.

In this chapter we have proposed two types of linguistic models for both classification and prediction problems. Mass relational methods build conditional models for each class or output focal set using the idea of a mass relation as described in chapter 4. These are then integrated into a Bayesian classification or estimation framework. Linguistic decision trees extend the decision tree formalism to include constraints on attributes defined by label expressions. Classification and prediction is then based on probabilities evaluated using Jeffrey’s rule.

The efficacy of these models was investigated from the dual perspectives of predictive accuracy and transparency. Both methods have been shown to give good accuracy across a number of classification and prediction problems with accuracy levels that are comparable with or better than a range of standard machine learning algorithms. In terms of transparency, mass relations generate sets of quantified atomic input-output conditional rules and can be used to evaluate queries or hypotheses expressed as multi-dimensional label expressions. Decision trees have a natural rule representation and the merging algorithm in LID3 allows for the generation of a wider range of descriptive rules than for mass relations. LDTs can also be used for query evaluation although they are slightly more limited than mass relations in this respect because of the difficulty of evaluating rules that are conditional on a constraint on the output attribute.

In the context of stroke therapy simulation, a method for the segmentation and reconstruction of human vasculature is presented and evaluated. Based on CTA scans, semi-automatic tools have been developed to reduce dataset noise, to segment using active contours, to extract the skeleton, to estimate the vessel radii and to reconstruct the associated surface. The robustness and accuracy of our technique are evaluated on a vascular phantom scanned in different orientations. The reconstructed surface is compared to a surface generated by marching cubes followed by decimation and smoothing. Experiments show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. The reconstructed surface is suitable for real-time simulation, interactive navigation and visualization.

In this chapter we have investigated non-additive appropriateness measures where the mass assignment values on labels sets are aggregated disjunctively using a t-conorm. Such measures are in fact generalisations of the additive measures studied throughout this volume since the latter can be obtained by selecting the t-conorm () = min (1, + ) and insisting that mass assignment values sum to one. A number of properties of generalised appropriateness measures have been studied, in particular for the special cases where is an Archimedean or Strict Archimedean t-conorm or where = max. In the latter case, referred to as possibilistic appropriateness measures, we identify a family of generalised mass selection functions for which the appropriateness measures of a conjunction of labels corresponds to a t-norm applied to the appropriateness measures of the conjuncts. Finally, we give an axiomatic characterization of generalised appropriateness measures and then discuss how best to interpret the failure of the law of excluded for these non-additive measures.