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Open-i

Screen detail of OpenI Web page
Project information

The Open-ism (pronounced “open eye”) experimental multimedia search engine retrieves and displays structured MEDLINE citations augmented by image-related text and concepts and linked to images based on image features.

The Open-i project provides novel information retrieval services for biomedical articles from the full text collections such as PubMed Central. It is unique in its ability to index both the text and images in the articles. The article retrieval is powered by the LHNCBC-built search engine Essie.

The Open-I biomedical image search engine lets users retrieve not only the MEDLINE citation information, but also the outcome statements in the article and the most relevant figure from it. Further, it is possible to use the figure as a query component to find other relevant images or other visually similar images. Future stages aim to provide image region-of-interest (ROI) based querying. The initial number of images is projected to be around 600,000 and will scale to millions. The extensive image analysis and indexing and deep text analysis and indexing require distributed computing. Future plans include making the image computation services available as a NLM service.

Publications/Tools: 
Apostolova E, You D, Xue Z, Antani SK, Demner-Fushman D, Thoma GR. Image Retrieval From Scientific Publications: Text and Image Content Processing To Separate Multipanel Figures. JASIST (Journal of the American Society for Information Science and Technology), 64(5):893–908, 2013.
You D, Rahman MM, Antani SK, Demner-Fushman D, Thoma GR. Text- and content-based biomedical image modality classification. Proc. SPIE Medical Imaging. Orlando, FL. February 2013;8674-21.
You D, Simpson M, Antani SK, Demner-Fushman D, Thoma GR. A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval. Proc. SPIE 8658, Document Recognition and Retrieval XX, 86580Q (February 4, 2013); doi:10.1117/12.2005934.
Antani SK, You D, Simpson M, Rahman M, Demner-Fushman D, Thoma GR. The role of image modality and visual characteristics in archiving biomedical images. Proc of IS&T Archiving Conference. Volume 2013, Number 1, 2013, pp. 31-35.
Rahman MM, You D, Simpson M. An interactive image retrieval framework for biomedical articles based on visual region-of-interest (ROI) identification and classification [Abstract]. The 2nd IEEE Conference on Healthcare Informatics, Imaging, and Systems Biology Analyzing Big Data for Healthcare and Biomedical Sciences (HISB 2012). La Jolla, CA. September 2012.
Simpson M, You D, Rahman MM, Demner-Fushman D, Antani SK, Thoma GR. ITI's participation in the ImageCLEF 2012 medical retrieval and classification tasks. CLEF 2012 Working Notes. Rome, Italy. September 2012.
Demner-Fushman D, Antani SK, Simpson M, Thoma GR. Design and Development of a Multimodal Biomedical Information Retrieval System JCSE. June 2012;6(2):168-177.
Simpson MS, You D, Rahman MM, Antani SK, Thoma GR, Demner-Fushman D. Towards the creation of a visual ontology of biomedical imaging entities. AMIA Annu Symp Proc. 2012;2012:866-75. Epub 2012 Nov 3.
You D, Antani SK, Demner-Fushman D, Govindaraju V, Thoma GR. Detecting Figure-Panel Labels In Medical Journal Articles Using MRF. Proceedings of 11th International Conference on Document Analysis and Recognition (ICDAR 2011). Beijing, China. September 2011:967-971.
Stanley RJ, De S, Demner-Fushman D, Antani SK, Thoma GR. An image feature-based approach to automatically find images for application to clinical decision support. Comput Med Imaging Graph. 2011 Jul;35(5):365-72. doi: 10.1016/j.compmedimag.2010.11.008. Epub 2010 Dec 8.

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