65 results about "Tree modeling" patented technology

A method for extracting a local center-axis representation of a vessel, includes: placing first and second seed points in an image that includes the vessel, wherein the first and second seed points are placed near a beginning and an end of a centerline of the vessel; representing the image as a discrete graph having nodes and edges, wherein the first seed point is a source node and the second seed point is a goal node; and finding a minimum-cost path between the first and second seed points by computing a cost of edges between the first and second seed points, wherein the cost of each edge is reciprocal to a vesselness measure of the edge.

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.

The invention discloses a hepatic vein vascular tree modeling method and a system thereof. The method comprises the following steps: using a liver model to obtain a liver image, and utilizing the multi-scale filtration method to strengthen a blood vessel; cutting a hepatic vein; extracting a central line of the hepatic vein; detecting and removing a link in the central line; and utilizing OSG / VTKto rebuild the hepatic vein vascular tree after pruning. The system comprises an image acquisition module, a blood vessel strengthening module, a blood vessel cutting module, a vascular tree central line extracting module and a vascular tree rebuilding module. The invention improves similarity functions in the filtering process, analyzes the characteristics of the ring and adopts corresponding unlinking methods for different links; and utilizes the relation of radius of blood vessel and the branch length when pruning. The invention effectively enhances the hepatic vein, improves the contrast between the blood vessel and liver parenchyma, and can extract more than five class branches, effectively unlink and prune the central line of the hepatic vein, rebuild the hepatic vein vascular tree and directly display the branches of the heptatic vein.