Medical Image Retrieval: Past and Present
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With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including textbased and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required.
Abstract
With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including textbased and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required.
참고문헌 (31)
- Liu Y., "A survey of contentbased image retrieval with high-level semantics", Pattern Recogn, 40, pp.262~282 (2007)
- Brahmi D., "Improving CBIR systems by integrating semantic features", Proceedings of the 1st Canadian Conference on Computer and Robot Vision, pp.233~240 (2004)
- Faloutsos C., "Efficient and effective querying by image content", J Intell Inf Syst, 3, pp.231~262 (1994)
- Pentland A., "Photobook : contentbased manipulation of image databases", Int J Comput Vis, 18, pp.233~254 (1996)
- Chang SF., "Visual information retrieval from large distributed online repositories", Commun ACM, 40, pp.63~71 (1997)
- Smith JR., "VisualSEEk: a fully automated content- based image query system", Proceedings of the fourth ACM international conference on Multimedia, pp.87~98 (1996)
- Ma WY., "NeTra: a toolbox for navigating large image databases", Proceedings of International Conference on Image Processing, pp.568~571 (1997)
- Eakins JP., "Towards intelligent image retrieval", Pattern Recogn, 35, pp.3~14 (2002)
- Brodley C., "Content-based retrieval from medical image databases: a synergy of human interaction.,machine learning and computer vision", Proceedings of the 16th National Conference on Artificial Intelligence and the 11th Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence, pp.760~767 (1999)
- Kelly PM., "Query by image example: the comparison algorithm for navigating digital image databases (CANDID) approach", Proceedings of the Storage and Retrieval for Image and Video Databases, pp.238~248 (1995)
- Orphanoudakis SC., "I2C : a system for the indexing., storage., and retrieval of medical images by content", Med Inform, 19, pp.109~122 (1994)
- Shyu CR., "ASSERT : a physician-in-the-loop contentbased retrieval system for HRCT image databases", Comput Vis Image Understand, 75, pp.111~132 (1999)
- Keysers D., "Statistical framework for model-based image retrieval in medical applications", J Electron Imaging, 12, pp.59~68 (2003)
- Deselaers T., "Features for Image Retrieva", pp.2003
- Muller H., "A review of content-based image retrieval systems in medical applications-clinical benefits and future directions", Int J Med Inform, 73, pp.1~23 (2004)
- "Fire" (2009)[URL]
- El-Kwae EA., "Content-based retrieval in picture archiving and communication systems", J Digit Imaging, 13, pp.70~81 (2000)
- Qi H., "Content-based image retrieval in picture archiving and communications systems", J Digit Imaging, 12, pp.81~83 (1999)
- Stearns MQ., "SNOMED clinical terms : overview of the development process and project status", Proc AMIA Symp, 2001, pp.662~666 (2001)
- Rosse C., "A reference ontology for biomedical informatics : the foundational model of anatomy", J Biomed Inform, 36, pp.478~500 (2003)
- Langlotz CP., "RadLex : a new method for indexing online educational materials", Radiographics, 26, pp.1595~1597 (2006)
- Rubin DL., "Creating and curating a terminology for radiology : ontology modeling and analysis", J Digit Imaging, 21, pp.355~362 (2008)
- Rubin DL., "Ontology-assisted analysis of Web queries to determine the knowledge radiologists seek", J Digit Imaging, 24, pp.160~164 (2011)
- Hazen R., "Automatic extraction of concepts to extend RadLex", J Digit Imaging, 24, pp.165~169 (2011)
- Luo B., "World Wide Web based image search engine using text and image content features", Proc SPIE, 5018, pp.123~130 (2003)
- Barrios JM., "Text-based and content-based image retrieval on Flickr: DEMO", Proceedings of the 2nd International Workshop on Similarity Search and Applications, pp.156~157 (2009)
- Deselaers T., "FIRE in ImageCLEF 2005: combining content-based image retrieval with textual information retrieval", Proceedings of the 6th International Conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories, pp.652~661 (2005)
- Dinakaran B., "Interactive image retrieval using text and image content", Cybern Inf Tech, 10, pp.20~30 (2010)
- Zhou XS., "Semantics and CBIR: a medical imaging perspective", Proceedings of the 2008 International Conference on Content-based Image and Video Retrieval, pp.571~580 (2008)
- Rubin DL., "Medical imaging on the semantic web : annotation and image markup", Proceedings of the Semantic Scientific Knowledge Integration (2008)
- Rubin DL., "iPad : semantic annotation and markup of radiological images", AMIA Annu Symp Proc, pp.626~630 (2008)
