ImageJ2: ImageJ for the next generation of scientific image data (1701.05940v4)

Abstract: 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.

• The paper introduces a comprehensive redesign of ImageJ to address modern imaging modalities and growing research needs.
• It outlines a modular architecture leveraging SciJava, ImgLib2, and ImageJ Ops to support complex image data with flexibility.
• The implementation ensures backward compatibility while boosting performance, scalability, and community-driven extensibility.

An In-Depth Review of "ImageJ2: ImageJ for the Next Generation of Scientific Image Data"

The paper ImageJ2: ImageJ for the Next Generation of Scientific Image Data presents a comprehensive overhaul of the original ImageJ application (referred to as ImageJ 1.x), which has been a cornerstone of scientific image analysis since its inception. This paper details the motivations, architectural redesign, technical implementations, and community impacts of ImageJ2, emphasizing how it addresses the evolving needs of scientific imaging.

Motivations for ImageJ2

The need for ImageJ2 arose from the limitations of ImageJ 1.x in handling emerging imaging modalities and meeting the demands of a rapidly growing user base with diverse needs. Key motivations include:

1. Support for Next-Generation Image Data: Modern imaging techniques generate highly complex datasets that go beyond simple 2D images. The original ImageJ infrastructure was inadequate for these advancements.
2. Facilitation of Software Collaborations: With the explosion of development tools and infrastructures, there is a need for modular and interoperable software frameworks.
3. Broadening Community and Use Cases: ImageJ, initially targeting the life sciences, has potential applications across various scientific disciplines. Ensuring broad utility requires improved flexibility and scalability.

Architectural Redesign

ImageJ2's architecture leverages modern software engineering principles to overcome the structural constraints of ImageJ 1.x. The design introduces several pivotal changes:

• Separation of Concerns: ImageJ2 decouples the data model from the user interface and computational logic, enabling flexible and modular development.
• Integration with External Applications: Emphasis on interoperability allows seamless integration with other scientific applications such as CellProfiler, KNIME, and OMERO.
• Extensible Plugin Framework: The redesigned plugin framework allows community-driven development, supporting everything from image formats to analytical methods.

Technical Implementation

The paper explores the core technical components of ImageJ2, organized hierarchically from foundational to high-level:

• SciJava Common: The lowest layer, providing essential services like plugin discovery, event handling, and modular configuration.
• ImgLib2: Facilitates a flexible and extensible container model, supporting various data types and structures beyond the original ImageJ's capabilities.
• SCIFIO: Manages reading and writing of diverse image formats, ensuring scalability and extensibility.
• ImageJ Ops: A library of image processing operations designed for high performance and ease of extension.

Performance and Compatibility

The paper underscores the importance of maintaining backwards compatibility with ImageJ 1.x while introducing more powerful ImageJ2 features. ImageJ Legacy plays a crucial role by facilitating runtime integration between ImageJ 1.x and ImageJ2 functionalities. Performance benchmarks are provided, demonstrating that the new architecture does not compromise efficiency and often enhances it.

Community and Extensibility

The ImageJ community is central to the success of both ImageJ and ImageJ2. Efforts made to ensure the continued relevance and utility of ImageJ include:

• Broad Plugin Ecosystem: Extensive plugin support enabled through a robust update mechanism ensures users can easily access and update tools.
• Online Resources and Documentation: A comprehensive wiki and discussion forum facilitate community engagement and knowledge sharing.
• Open Development: Embracing open source principles, ImageJ2 encourages contributions through public repositories and open issue tracking.

Future Directions

The paper outlines several future directions for ImageJ2, including:

• Enhanced Metadata and ROI Support: Finalizing the data model to support extensive metadata and ROI types.
• Improved I/O Mechanisms: Refining SCIFIO and ImgLib2 to handle arbitrary blocks and enhance scalability.
• Cloud Computing Integration: Leveraging cloud platforms for scalable image processing.
• Web UI Development: Implementing a REST-based web interface for broader accessibility.

Conclusion

ImageJ2 represents a significant advancement over ImageJ 1.x, reimagining it as not just a standalone application but a versatile framework for scientific image analysis. By addressing the limitations of ImageJ 1.x and incorporating modern software engineering practices, ImageJ2 stands poised to support the next generation of scientific imaging challenges. Its architecture fosters community involvement, ensuring its continuous evolution and relevance in diverse scientific fields.