{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/barcode-annotations-for-medical-image","title":"Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation","arxiv_id":"1505.05212","date":"2015-05-19","proceeding":null,"authors":["Hamid. R. Tizhoosh"],"abstract":"This paper proposes to generate and to use barcodes to annotate medical\nimages and/or their regions of interest such as organs, tumors and tissue\ntypes. A multitude of efficient feature-based image retrieval methods already\nexist that can assign a query image to a certain image class. Visual\nannotations may help to increase the retrieval accuracy if combined with\nexisting feature-based classification paradigms. Whereas with annotations we\nusually mean textual descriptions, in this paper barcode annotations are\nproposed. In particular, Radon barcodes (RBC) are introduced. As well, local\nbinary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as\nbarcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test\nimages is used to verify how barcodes could facilitate image retrieval.","url_abs":"http://arxiv.org/abs/1505.05212v1","url_pdf":"http://arxiv.org/pdf/1505.05212v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"barcode-annotations-for-medical-image","repo_url":"https://github.com/hungyiwu/mixed-distance","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"medical-image-retrieval","task_name":"Medical Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}