Subdivision surface modeling system based on arbitrary topological curves network and combined subdivision

Arbitrary topological curve network has no restriction in topology structure,so it has more powerful representing ability in defining complex surfaces.A complex surface modeling system is presented based on arbitrary topological curve network and the improved combined subdivision method,its functions including creating and editing curve network,and generating and modifying curve network＇s interpolated surface.This modeling system can be used to the process of products＇concept design,and its applications is also significant to the development of subdivision method.

Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors

AIM:To study the role of time-intensity curve(TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.METHODS:We prospectively included 112 patients with hepatocellular carcinoma(HCC)(n = 41),hypervascular(n = 20) and hypovascular(n = 12) liver metastases,hepatic hemangiomas(n = 16) or focal fatty changes(n = 23) who underwent contrast-enhanced ultrasonography in the Research Center of Gastroenterology and Hepatology,Craiova,Romania.We recorded full length movies of all contrast uptake phases and post-processed them offline by selecting two areas of interest(one for the tumor and one for the healthy surrounding parenchyma) and consecutive TIC analysis.The difference in maximum intensities,the time to reaching them and the aspect of the late/portal phase,as quantified by the neural network and a ratio between median intensities of the central and peripheral areas were analyzed by a feed forward back propagation multi-layer neural network which was trained to classify data into five distinct classes,corresponding to each type of liver lesion.RESULTS:The neural network had 94.45% training accuracy(95% CI:89.31%-97.21%) and 87.12% testing accuracy(95% CI:86.83%-93.17%).The automatic classification process registered 93.2% sensitivity,89.7% specificity,94.42% positive predictive value and 87.57% negative predictive value.The artificial neural networks(ANN) incorrectly classified as hemangyomas three HCC cases and two hypervascular metastases,while in turn misclassifying four liver hemangyomas as HCC(one case) and hypervascular metastases(three cases).Comparatively,human interpretation of TICs showed 94.1% sensitivity,90.7% specificity,95.11% positive predictive value and 88.89% negative predictive value.The accuracy and specificity of the ANN diagnosis system was similar to that of human interpretation of the TICs(P = 0.225 and P = 0.451,respectively).Hepatocellular carcinoma cases showed contrast uptake during the arterial phase followed by wash-out in the portal and first seconds of the late phases.For the hypovascular metastases did not show significant contrast uptake during the arterial phase,which resulted in negative differences between the maximum intensities.We registered wash-out in the late phase for most of the hypervascular metastases.Liver hemangiomas had contrast uptake in the arterial phase without agent wash-out in the portallate phases.The focal fatty changes did not show any differences from surrounding liver parenchyma,resulting in similar TIC patterns and extracted parameters.CONCLUSION:Neural network analysis of contrastenhanced ultrasonography-obtained TICs seems a promising field of development for future techniques,providing fast and reliable diagnostic aid for the clinician.

Topology Recovery Technique for Complex Freeform Surface Model after Local Geometry Repair

Intersections and discontinuities commonly arise in surface modeling and cause problems in downstream operations. Local geometry repair, such as cover holes or replace bad surfaces by adding new surface patches for dealing with inconsistencies among the confluent region, where multiple surfaces meet, is a common technique used in CAD model repair and reverse engineering. However, local geometry repair destroys the topology of original CAD model and increases the number of surface patches needed for freeform surface shape modeling. Consequently, a topology recovery technique dealing with complex freeform surface model after local geometry repair is proposed. Firstly, construct the curve network which freeform surface model; secondly, apply freeform surface fitting method determine the geometry and topology properties of recovery to create B-spline surface patches to recover the topology of trimmed ones. Corresponding to the two levels of enforcing boundary conditions on a B-spline surface, two solution schemes are presented respectively. In the first solution scheme, non-constrained B-spline surface fitting method is utilized to piecewise recover trimmed confluent surface patches and then employs global beautification technique to smoothly stitch the recovery surface patches. In the other solution scheme, constrained B-spline surface fitting technique based on discretization of boundary conditions is directly applied to recover topology of surface model after local geometry repair while achieving G~ continuity simultaneously. The presented two different schemes are applied to the consistent surface model, which consists of five trimmed confluent surface patches and a local consistent surface patch, and a machine cover model, respectively. The application results show that our topology recovery technique meets shape-preserving and Gt continuity requirements in reverse engineering. This research converts the problem of topology recovery for consistent surface model to the problem of constructing G1 patches from a given curve network, and provides a new idea to model repairing study.

An Effective Surface Modeling Method for Car Styling from a Side-View Image

We introduce an almost-automatic technique for generating 3D car styling surface models based on a single side-view image. Our approach combines the prior knowledge of car styling and deformable curve network model to obtain an automatic modeling process. Firstly, we define the consistent parameterized curve template for 2D and 3D case respectivelyby analyzingthe characteristic lines for car styling. Then, a semi-automatic extraction from a side-view car image is adopted. Thirdly, statistic morphable model of 3D curve network isused to get the initial solution with sparse point constraints.Withonly afew post-processing operations, the optimized curve network models for creating surfaces are obtained. Finally, the styling surfaces are automatically generated using template-based parametric surface modeling method. More than 50 3D curve network models are constructed as the morphable database. We show that this intelligent modeling toolsimplifiesthe exhausted modeling task, and also demonstratemeaningful results of our approach.