{"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/2d-and-3d-vascular-structures-enhancement-via","title":"2D and 3D Vascular Structures Enhancement via Multiscale Fractional Anisotropy Tensor","arxiv_id":"1902.00550","date":"2019-02-01","proceeding":null,"authors":["Haifa F. Alhasson","Shuaa S. Alharbi","Boguslaw Obara"],"abstract":"The detection of vascular structures from noisy images is a fundamental\nprocess for extracting meaningful information in many applications. Most\nwell-known vascular enhancing techniques often rely on Hessian-based filters.\nThis paper investigates the feasibility and deficiencies of detecting\ncurve-like structures using a Hessian matrix. The main contribution is a novel\nenhancement function, which overcomes the deficiencies of established methods.\nOur approach has been evaluated quantitatively and qualitatively using\nsynthetic examples and a wide range of real 2D and 3D biomedical images.\nCompared with other existing approaches, the experimental results prove that\nour proposed approach achieves high-quality curvilinear structure enhancement.","url_abs":"http://arxiv.org/abs/1902.00550v1","url_pdf":"http://arxiv.org/pdf/1902.00550v1.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":"2d-and-3d-vascular-structures-enhancement-via","repo_url":"https://github.com/Haifafh/MFAT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}