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

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

/pdf/the-multimodal-brain-tumor-image-segmentation-benchmark-2gfraj98dk.pdf

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

A reproducible evaluation of ANTs similarity metric performance in brain image registration.

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. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. 
We present ImageJ2, a total redesign of ImageJ offering 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. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

This paper proposes 2D, 3D and 4D discrete sampling grids with optimal topological and spectral properties. It is shown here that those grids have advantages with respect to the classically used ℤn grid. The proposed 3D grids are used to achieve surface extraction from volume data. Results are shown for a medical imaging application.

/pdf/determination-of-discrete-sampling-grids-with-optimal-57pnxkyn09.pdf

Determination of discrete sampling grids with optimal topological and spectral properties

Needle steering is a commonly used technique in the medical field as it enables physicians to more precisely reach the target tissue. In this paper we describe our interest in needle steering for lung biopsy and the significance of this technique. There has been much interest in modeling needle steering in recent years and this paper builds upon that work. We describe our Matlab implementation and present simulation results. We also show our experimental results based on a robotic needle driver and X-Ray imaging in the interventional suite. The experimental results showed good agreement with the simulation results.

http://urobotics.urology.jhu.edu/pub/2008-ding-bibe.pdf

Medical needle steering for lung biopsy: Experimental results in tissue phantoms using a robotic needle driver

The practice of image processing inherently requires software development. Creating this technology requires designing, implementing, debugging and testing software applications on a continual basis. Furthermore current software development is typically performed in a distributed environment involving many developers. While the use of open-source software may create collaborative communities that enhance overall technology exchange, it does nothing directly to manage change nor does it address the quality of the underlying software. This paper describes a software development process that has proven vital to the success of the widely used open-source toolkits ITK (itk.org) and VTK (vtk.org). This process facilitates cross-platform development, includes automatic documentation generation, integrates continuous testing, and posts the results of the process on publicly accessible Web pages. The net result is that a responsive feedback loop is created between the developers in the community and automated processes to measure software quality. With this process software converges towards better software as long as the process is enforced. The tools described here are open-source and available for use in academic and commercial applications.

Software process: the key to developing robust, reusable and maintainable open-source software

Integrating segmentation methods from the Insight Toolkit into a visualization application

A formal definition of a combinatorial topology is presented in this paper for the discrete N-dimensional space defined by the An* lattice. The use of this grid instead of the classical n is based on two arguments: 
It is the optimal sampling grid in the sense of Shannon's sampling theorem in 2 and 3 dimensions,
It induces the simplest discrete topology definition because its dual is a K-simplex.



Their interest in image processing and in the medical imaging field is presented with some examples.

Luis Ibanez

Papers

Determination of discrete sampling grids with optimal topological and spectral properties

Medical needle steering for lung biopsy: Experimental results in tissue phantoms using a robotic needle driver

Software process: the key to developing robust, reusable and maintainable open-source software

Integrating segmentation methods from the Insight Toolkit into a visualization application

Discrete Topology of (An*) Optimal Sampling Grids. Interest in Image Processing and Visualization