Managing Biomedical Image Metadata for Search and Retrieval of Similar Images.
Radiology 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...
| Published in: | Journal of Digital Imaging Vol. 24; no. 4; pp. 739 - 749 |
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| Main Authors: | , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2011
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104661557&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104661557 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2011 vid: 24 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104661557 62871081 10.1007/s10278-010-9328-z NLM20844917 104661557 ppf: 739 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Managing Biomedical Image Metadata for Search and Retrieval of Similar Images. aug: au: Korenblum, Daniel Rubin, Daniel Napel, Sandy Rodriguez, Cesar Beaulieu, Chris affil: Department of Radiology, Stanford University, Stanford USA sug: subj: Metadata Administration Picture Archiving and Communication Systems Image Retrieval Systems Human Evaluation Research Funding Source Systems Design Markup Languages Internet Protocols DICOM Retrospective Design Sensitivity and Specificity ROC Curve ab: Radiology 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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