http://midag.cs.unc.edu/pubs/papers/Neuroimage06_Yushkevich.pdf

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Gmsh is an open-source 3-D finite element grid generator with a build-in CAD engine and post-processor. Its design goal is to provide a fast, light and user-friendly meshing tool with parametric input and advanced visualization capabilities. This paper presents the overall philosophy, the main design choices and some of the original algorithms implemented in Gmsh. Copyright (C) 2009 John Wiley & Sons, Ltd.

Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities

3D Slicer as an image computing platform for the Quantitative Imaging Network.

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

/pdf/imagej2-imagej-for-the-next-generation-of-scientific-image-41ybjcosb6.pdf

ImageJ2: ImageJ for the next generation of scientific image data

Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplification algorithm which can rapidly produce high quality approximations of polygonal models. The algorithm uses iterative contractions of vertex pairs to simplify models and maintains surface error approximations using quadric matrices. By contracting arbitrary vertex pairs (not just edges), our algorithm is able to join unconnected regions of models. This can facilitate much better approximations, both visually and with respect to geometric error. In order to allow topological joining, our system also supports non-manifold surface models. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—surface and object representations

/pdf/surface-simplification-using-quadric-error-metrics-r9zm14jwq8.pdf

Surface simplification using quadric error metrics

The polygon remains a popular graphics primitive for computer graphics application. Besides having a simple representation, computer rendering of polygons is widely supported by commercial graphics hardware and software. However, because the polygon is linear, often thousands or millions of primitives are required to capture the details of complex geometry. Models of this size are generally not practical since rendering speeds and memory requirements are proportional to the number of polygons. Consequently applications that generate large polygonal meshes often use domain-specific knowledge to reduce model size. There remain algorithms, however, where domainspecific reduction techniques are not generally available or appropriate. One algorithm that generates many polygons is marching cubes. Marching cubes is a brute force surface construction algorithm that extracts isodensity surfaces from volume data, producing from one to five triangles within voxels that contain the surface. Although originally developed for medical applications, marching cubes has found more frequent use in scientific visualization where the size of the volume data sets are much smaller than those found in medical applications. A large computational fluid dynamics volume could have a finite difference grid size of order 100 by 100 by 100, while a typical medical computed tomography or magnetic resonance scanner produces over 100 slices at a resolution of 256 by 256 or 512 by 512 pixels each. Industrial computed tomography, used for inspection and analysis, has even greater resolution, varying from 512 by 512 to 1024 by 1024 pixels. For these sampled data sets, isosurface extraction using marching cubes can produce from 500k to 2,000k triangles. Even today’s graphics workstations have trouble storing and rendering models of this size. Other sampling devices can produce large polygonal models: range cameras, digital elevation data, and satellite data. The sampling resolution of these devices is also improving, resulting in model sizes that rival those obtained from medical scanners. This paper describes an application independent algorithm that uses local operations on geometry and topology to reduce the number of triangles in a triangle mesh. Although our implementation is for the triangle mesh, it can be directly applied to the more general polygon mesh. After describing other work related to model creation from sampled data, we describe the triangle decimation process and its implementation. Results from two different geometric modeling applications illustrate the strengths of the algorithm.

/pdf/decimation-of-triangle-meshes-3fvv4shbtq.pdf

Decimation of triangle meshes

We present the detailed planning and execution of the Insight Toolkit (ITK), an application programmers interface (API) for the segmentation and registration of medical image data. This public resource has been developed through the NLM Visible Human Project, and is in beta test as an open-source software offering under cost-free licensing. The toolkit concentrates on 3D medical data segmentation and registration algorithms, multimodal and multiresolution capabilities, and portable platform independent support for Windows, Linux/Unix systems. This toolkit was built using current practices in software engineering. Specifically, we embraced the concept of generic programming during the development of these tools, working extensively with C++ templates and the freedom and flexibility they allow. Software development tools for distributed consortium-based code development have been created and are also publicly available. We discuss our assumptions, design decisions, and some lessons learned.

Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.

Triangle decimation techniques reduce the number of triangles in a mesh, typically to improve interactive rendering performance or reduce data storage and transmission requirements. Most of these algorithms are designed to preserve the original topology of the mesh. Unfortunately, this characteristic is a strong limiting factor in overall reduction capability, since objects with a large number of holes or other topological constraints cannot be effectively reduced. The author presents an algorithm that yields a guaranteed reduction level, modifying topology as necessary to achieve the desired result. In addition, the algorithm is based on a fast local decimation technique, and its operations can be encoded for progressive storage, transmission, and reconstruction. He describes the new progressive decimation algorithm, introduces mesh splitting operations and shows how they can be encoded as a progressive mesh. He also demonstrates the utility of the algorithm on models ranging in size from 1,132 to 1.68 million triangles and reduction ratios of up to 200:1.

/pdf/a-topology-modifying-progressive-decimation-algorithm-3qvrp2s85e.pdf

A topology modifying progressive decimation algorithm

One approach to fully automatic mesh generation in two and three dimensions is to generate and triangulate a set of points within and on the boundary of a geometry using the properties of the Delaunay triangulation. Because the point data generate mesh topology of greater dimension, it is necessary to insure topological compatibility and perform classification of the resulting mesh with respect to the original geometry. Necessary and sufficient conditions for classification of mesh topology on three-dimensional planar geometry are given, as well as a discussion of the more complex case of curved geometry. This paper also presents conditions to insure topological compatibility along with techniques for identifying and resolving cases of incompatibility.

Geometry-based fully automatic mesh generation and the delaunay triangulation

We present a new method for the visualization of 3D vector fields called the Stream polygon: a regular, n-sided polygon, oriented normal to the local vector, The polygon can represent local deformation due to rigid body rotation and both normal and shear strain. In addition, the effects of translation and scalar functions may be represented by sweeping the stream polygon along the streamline, and by appropriately varying the radius and shading the surface of the resulting streamtube. In this paper we develop a mathematical foundation for the stream polygon, and provide examples with application to velocity field visualization.

The stream polygon-a technique for 3D vector field visualization

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