Boostlet.Js: Medical Image Processing Plugins for the Web via Javascript Injection
Can web-based image processing and visualization tools easily integrate into existing websites without significant time and effort? Our Boostlet.js library addresses this challenge by providing an open-source, JavaScript-based web framework to enable additional image processing functionalities. Boost-let examples include kernel filtering, image captioning, data visualization, segmentation, and web-optimized machine-learning models. To achieve this, Boostlet.js uses a browser bookmark to inject a user-friendly plugin selection tool called PowerBoost into any host website. Boostlet provides on-site access to a standard API independent of any visualization framework for pixel data and scene manipulation. Boostlet is natively integrated into SliceDrop, a web-based, interactive viewer for medical imaging data. Boostlets provide a modular architecture and client-side processing capabilities to apply advanced image-processing techniques using consumer-level hardware. The code is open-source and available.
Authors
- SangHyuk Kim
- Daniel Haehn (ORCID: https://orcid.org/0000-0001-9144-3461)
- Tim De Meyer (ORCID: https://orcid.org/0000-0003-2994-9693)
- Edward Gaibor
- Vidhya Sree Narayanappa
- Shruti Varade
- Mahsa Geshvadi (ORCID: https://orcid.org/0009-0006-0768-526X)
- Rohini Deshmukh
Institutions
- University of Massachusetts Boston (US)
- Universität der Bundeswehr München (DE)
Publication Details
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1109/isbi60581.2025.10981020
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00