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. 2001 Sep-Oct;8(5):431-42.
doi: 10.1136/jamia.2001.0080431.

BrainImageJ: a Java-based framework for interoperability in neuroscience, with specific application to neuroimaging

Y R Ng  1 , S Shiffman, T J Brosnan, J M Links, L S Beach, N S Judge, Y Xu, U V Kelkar, A L Reiss
Affiliations

BrainImageJ: a Java-based framework for interoperability in neuroscience, with specific application to neuroimaging

Y R Ng et al. J Am Med Inform Assoc. 2001 Sep-Oct.

Abstract

The Human Brain Project consortium continues to struggle with effective sharing of tools. To facilitate reuse of its tools, the Stanford Psychiatry Neuroimaging Laboratory (SPNL) has developed BrainImageJ, a new software framework in Java. The framework consists of two components-a set of four programming interfaces and an application front end. The four interfaces define extension pathways for new data models, file loaders and savers, algorithms, and visualization tools. Any Java class that implements one of these interfaces qualifies as a BrainImageJ plug-in-a self-contained tool. After automatically detecting and incorporating new plug-ins, the application front end transparently generates graphical user interfaces that provide access to plug-in functionality. New plug-ins interoperate with existing ones immediately through the front end. BrainImageJ is used at the Stanford Psychiatry Neuroimaging Laboratory to develop image-analysis algorithms and three-dimensional visualization tools. It is the goal of our development group that, once the framework is placed in the public domain, it will serve as an interlaboratory platform for designing, distributing, and using interoperable tools.

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Figures

Figure 1
Figure 1
The BrainImageJ architecture, and its relationship to developers and end-users. Plug-ins in the file loader and saver, algorithm and visualization extension pathways communicate in terms of a common data-model plug-in. The front end automatically incorporates new plug-ins, and provides them with transparent support for modern application features.
Figure 2
Figure 2
A visualization plug-in that displays axial slices of a DataCells3D model. The bottom right pane shows an interactive three-dimensional composition of the three slices.
Figure 3
Figure 3
Surface Editor, a visualization plug-in that renders Surface objects. The inner and outer cortical surfaces (top right and bottom right) extracted with the Isosurface Extraction Algorithm. A composite, interactive view (left), which also depicts magnified surface points corresponding to mouse clicks (spheres), and a gyrus segmented from the outer cortical surface (opaque white).
Figure 4
Figure 4
The processing pipe for expert-based segmentation of a gyrus from MR data using the surface-drawing BrainImageJ plug-ins.
Figure 5
Figure 5
An example of plug-in reuse in the development cycle: an application to correct image artifacts in MR data caused by field inhomogeneity, in which developers reuse three plug-ins and implement one new algorithm plug-in.
Figure 6
Figure 6
Comparative performance of basic computation, file IO and 3D graphics rendering for SunTM JVMs on Windows, Linux and Solaris.
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