{"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/a-modified-fuzzy-c-means-algorithm-for","title":"A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images","arxiv_id":"1801.05694","date":"2018-01-17","proceeding":null,"authors":["Awais Ashfaq","Jonas Adler"],"abstract":"CBCT images suffer from acute shading artifacts primarily due to scatter.\nNumerous image-domain correction algorithms have been proposed in the\nliterature that use patient-specific planning CT images to estimate shading\ncontributions in CBCT images. However, in the context of radiosurgery\napplications such as gamma knife, planning images are often acquired through\nMRI which impedes the use of polynomial fitting approaches for shading\ncorrection. We present a new shading correction approach that is independent of\nplanning CT images. Our algorithm is based on the assumption that true CBCT\nimages follow a uniform volumetric intensity distribution per material, and\nscatter perturbs this uniform texture by contributing cupping and shading\nartifacts in the image domain. The framework is a combination of fuzzy C-means\ncoupled with a neighborhood regularization term and Otsu's method. Experimental\nresults on artificially simulated craniofacial CBCT images are provided to\ndemonstrate the effectiveness of our algorithm. Spatial non-uniformity is\nreduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With\nshading-correction, thresholding based segmentation accuracy for bone pixels is\nimproved from 85% to 91% when compared to thresholding without\nshading-correction. The proposed algorithm is thus practical and qualifies as a\nplug and play extension into any CBCT reconstruction software for shading\ncorrection.","url_abs":"http://arxiv.org/abs/1801.05694v1","url_pdf":"http://arxiv.org/pdf/1801.05694v1.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":"a-modified-fuzzy-c-means-algorithm-for","repo_url":"https://github.com/adler-j/mfcm_article","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"a-modified-fuzzy-c-means-algorithm-for","repo_url":"https://github.com/odlgroup/odl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MPL-2.0"}}],"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}