{"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/automated-breast-lesion-segmentation-in","title":"Automated Breast Lesion Segmentation in Ultrasound Images","arxiv_id":"1609.08364","date":"2016-09-27","proceeding":null,"authors":["Ibrahim Sadek","Mohamed Elawady","Viktor Stefanovski"],"abstract":"The main objective of this project is to segment different breast ultrasound\nimages to find out lesion area by discarding the low contrast regions as well\nas the inherent speckle noise. The proposed method consists of three stages\n(removing noise, segmentation, classification) in order to extract the correct\nlesion. We used normalized cuts approach to segment ultrasound images into\nregions of interest where we can possibly finds the lesion, and then K-means\nclassifier is applied to decide finally the location of the lesion. For every\noriginal image, an annotated ground-truth image is given to perform comparison\nwith the obtained experimental results, providing accurate evaluation measures.","url_abs":"http://arxiv.org/abs/1609.08364v1","url_pdf":"http://arxiv.org/pdf/1609.08364v1.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":"automated-breast-lesion-segmentation-in","repo_url":"https://github.com/mawady/bus-segmentation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}