{"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-geometric-approach-for-fully-automatic","title":"A Geometric Approach For Fully Automatic Chromosome Segmentation","arxiv_id":"1112.4164","date":"2011-12-18","proceeding":null,"authors":["Shervin Minaee","Mehran Fotouhi","Babak Hossein Khalaj"],"abstract":"A fundamental task in human chromosome analysis is chromosome segmentation.\nSegmentation plays an important role in chromosome karyotyping. The first step\nin segmentation is to remove intrusive objects such as stain debris and other\nnoises. The next step is detection of touching and overlapping chromosomes, and\nthe final step is separation of such chromosomes. Common methods for separation\nbetween touching chromosomes are interactive and require human intervention for\ncorrect separation between touching and overlapping chromosomes. In this paper,\na geometric-based method is used for automatic detection of touching and\noverlapping chromosomes and separating them. The proposed scheme performs\nsegmentation in two phases. In the first phase, chromosome clusters are\ndetected using three geometric criteria, and in the second phase, chromosome\nclusters are separated using a cut-line. Most of earlier methods did not work\nproperly in case of chromosome clusters that contained more than two\nchromosomes. Our method, on the other hand, is quite efficient in separation of\nsuch chromosome clusters. At each step, one separation will be performed and\nthis algorithm is repeated until all individual chromosomes are separated.\nAnother important point about the proposed method is that it uses the geometric\nfeatures of chromosomes which are independent of the type of images and it can\neasily be applied to any type of images such as binary images and does not\nrequire multispectral images as well. We have applied our method to a database\ncontaining 62 touching and partially overlapping chromosomes and a success rate\nof 91.9% is achieved.","url_abs":"http://arxiv.org/abs/1112.4164v5","url_pdf":"http://arxiv.org/pdf/1112.4164v5.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-geometric-approach-for-fully-automatic","repo_url":"https://github.com/LilyHu/image_segmentation_chromosomes","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-geometric-approach-for-fully-automatic","repo_url":"https://github.com/jeanpat/DeepFISH","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"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}