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Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging
January/February 2009 (vol. 24 no. 1)
pp. 57-65
ASCII Text
x 
 
Daniel L. Rubin, Pattanasak Mongkolwat, Vladimir Kleper, Kaustubh Supekar, David S. Channin, "Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging," IEEE Intelligent Systems, vol. 24, no. 1, pp. 57-65, January/February, 2009.
 
 
BibTex
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@article{ 10.1109/MIS.2009.3,
author = {Daniel L. Rubin and Pattanasak Mongkolwat and Vladimir Kleper and Kaustubh Supekar and David S. Channin},
title = {Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging},
journal ={IEEE Intelligent Systems},
volume = {24},
number = {1},
issn = {1541-1672},
year = {2009},
pages = {57-65},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2009.3},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging
IS - 1
SN - 1541-1672
SP57
EP65
EPD - 57-65
A1 - Daniel L. Rubin,
A1 - Pattanasak Mongkolwat,
A1 - Vladimir Kleper,
A1 - Kaustubh Supekar,
A1 - David S. Channin,
PY - 2009
KW - intelligent web services
KW - semantic web
KW - ontology design
KW - image representation
VL - 24
JA - IEEE Intelligent Systems
ER -
 
Daniel L. Rubin, Stanford University
Pattanasak Mongkolwat, Northwestern University
Vladimir Kleper, Northwestern University
Kaustubh Supekar, Stanford University
David S. Channin, Northwestern University
Medical images are proliferating at an explosive pace, similar to other types of data in e-Science. While Semantic Web techniques are being created to access much raw biomedical data, the rich information in images is not currently accessible. We are creating methods and tools to enable people to access large distributed collections of medical images in cyberspace as well as within hospital information systems. In this report, we describe our approach, "Annotation and Image Markup" (AIM), in which human and machine descriptions of image content is made explicit and accessible using ontologies. AIM includes the following components: an ontology of image annotation and markup, specifying entities and relations to represent the semantics of images; an image annotation tool to collect annotations from people viewing images as instances of the ontology; and a serialization module to store the image annotation information in a variety of standard formats, enabling interoperability among a variety of systems that contain images: medical records systems, image archives in hospitals, and the Semantic Web. Through these methods, we hope to enable the scientific community who work with images to access their semantic contents and integrate them with related non-imaging information so they exploit the image information effectively.

1. O. Bodenreider and R. Stevens, "Bio-ontologies: Current Trends and Future Directions," Briefings in Bioinformatics, vol. 7, no. 3, Sept. 2006, pp. 256–274.
2. A. Ruttenberg et al., "Advancing Translational Research with the Semantic Web," BMC Bioinformatics,9 May 2007, p. S2.
3. R. Troncy et al., "Image Annotation on the Semantic Web," World Wide Web Consortium (W3C) Incubator Group Report, 14 Aug. 2007; www.w3.org/2005/Incubator/mmsemXGR-image-annotation.
4. D.L. Rubin et al., "Medical Imaging on the Semantic Web: Annotation and Image Markup," Proc. 2008 AAAI Spring Symp. Series, Semantic Scientific Knowledge Integration, AAAI Press, 2008.
5. C.P. Langlotz, "RadLex: A New Method for Indexing Online Educational Materials," Radiographics, Nov.–Dec. 2006, pp. 1595–1597.
6. D.L. Rubin, "Creating and Curating a Terminology for Radiology: Ontology Modeling and Analysis," J. Digital Imaging,15 Sept. 2007.
7. R.D. Shankar et al., "An Ontology-based Architecture for Integration of Clinical Trials Management Applications," AMIA Ann. Symp. Proc. 2007, Am. Medical Informatics Assoc., 2007, pp. 661–665.
8. E. Camon et al., "The Gene Ontology Annotation (GOA) Project-Application of GO in SWISS-PROT, TrEMBL and InterPro," Comparative and Functional Genomics, vol. 4. no. 1, 2003, pp. 71–74.
9. P. Rafferty and R. Hidderley, "Flickr and Democratic Indexing: Dialogic Approaches to Indexing," Aslib Proc., vol. 59, nos. 4–5, pp. 397–410.
10. A. Mueen, R. Zainuddin, and M.S. Baba, "Automatic Multilevel Medical Image Annotation and Retrieval," J. Digital Imaging, Springer, 11 Sept. 2007, pp. 290–295.
11. D.L. Rubin, O. Dameron, and M.A. Musen, "Use of Description Logic Classification to Reason about Consequences of Penetrating Injuries," AMIA Ann. Symp. Proc. 2005, Am. Medical Informatics Assoc., 2005, pp. 649–653.

Index Terms:
intelligent web services, semantic web, ontology design, image representation
Citation:
Daniel L. Rubin, Pattanasak Mongkolwat, Vladimir Kleper, Kaustubh Supekar, David S. Channin, "Annotation and Image Markup: Accessing and Interoperating with the Semantic Content in Medical Imaging," IEEE Intelligent Systems, vol. 24, no. 1, pp. 57-65, Jan.-Feb. 2009, doi:10.1109/MIS.2009.3
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