Using SVG to model and query image annotations and their history

Image annotation is becoming increasingly important for clinical applications and medical research. Especially, collaborative image annotation can harness the collective intelligence from distributed users. There are several challenges to model medical image annotations: i) Medical image annotation includes not only textual annotation but also graphical annotation, which can consist of multiple areas or layers; ii) To support collaborative annotation from multiple users, collaboration has to be supported and represented in a proper way; iii) Both textual and graphical annotations should be searchable, e.g., support of spatial queries; iv) annotation history needs to be tracked and searchable in applications such as clinical trials; and v) To enable convenient annotation, and save cost, lightweight system should be provided. Most current medical image annotation applications are workstation based applications, and use proprietary methods to represent annotations, which make it difficult to search, share, and exchange annotations. Meanwhile, the Web is evolving quickly on supporting strong collaboration through several new technologies, including Ajax (Asynchronous JavaScript and XML) and SVG (Scalable Vector Graphics). Especially, SVG now becomes a standard language for vector graphics on the Web, which, indeed, is recently natively supported by latest Web browsers. This brings a new opportunity to support collaborative image annotations on the Web. In our work, we develop a comprehensive data model to support both textual and graphical annotations and their history on 2-D medical images, and implement the data model with the standard language SVG. This data model can support powerful queries on annotations including complex textual, spatial, collaboration, group, and history queries based on standard XML Query language XQuery. Besides, the approach can be used to support collaborative image annotations in a lightweight Web browser environment.