Continued thread
this paper cites one of my favorite publications. a first-authored paper which presented some of my work in Stanford's Radiology department.
to our knowledge, i built the world's 1st clinically-suited search engine for biomedical images (think: DICOM server) that could be searched using structured metadata provided by the radiologists reading the CT or MRI images, using open-source standards. #Ruby #RoR #REST #HTML5
SpringerLinkManaging Biomedical Image Metadata for Search and Retrieval of Similar Images - Journal of Imaging Informatics in MedicineRadiology images are generally disconnected from the metadata describing their contents, such as imaging observations (“semantic” metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system’s “match observations” function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.
大部分RESTful API 並非真正的RESTful
