Vispack

technical reportVISPACK (volume-image-surface package) is a C++ library that includes matrix, image, and volume objects and tools for manipulating level-set surface model

Isosurfaces and level-set surface models

technical reportThis paper is a set of notes that present the basic geometry of isosurfaces and the basic methods for using level sets to model deformable surfaces. It begins with a short introduction to isosurface geometry, including curvature. It continues with a short explanation of the level-set partial differential equations. It also presents some practical details for how to solve these equations using up-wind scheme and sparse calculation methods. This paper presents a series of examples of how level-set surface models are used to solve problems in graphics and vision. Finally, it presents some examples of implementations using VISPack, an object oriented, C++ library for doing volume processing and level-set surface modeling

Realtime image denoising of fluoroscopy images for Dose Reduction

poste

A fast iterative method for solving the eikonal equation on triangulated surfaces

poste

Curve boxplot: Generalization of boxplot for ensembles of curves

pre-printIn simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics

I Know What's Moved!

poste

A FAST ITERATIVE METHOD FOR EIKONAL EQUATIONS

In this paper we propose a novel computational technique to solve the Eikonal equation efficiently on parallel architectures. The proposed method manages the list of active nodes and iteratively updates the solutions on those nodes until they converge. Nodes are added to or removed from the list based on a convergence measure, but the management of this list does not entail an extra burden of expensive ordered data structures or special updating sequences. The proposed method has suboptimal worst-case performance but, in practice, on real and synthetic datasets, runs faster than guaranteed-optimal alternatives. Furthermore, the proposed method uses only local, synchronous updates and therefore has better cache coherency, is simple to implement, and scales efficiently on parallel architectures. This paper describes the method, proves its consistency, gives a performance analysis that compares the proposed method against the state-of-the-art Eikonal solvers, and describes the implementation on a single instruction multiple datastream (SIMD) parallel architecture.open393

Multiatlas segmentation as nonparametric regression

pre-printThis paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation.We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator.We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems