{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:14:43Z","timestamp":1772165683672,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T00:00:00Z","timestamp":1628121600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T00:00:00Z","timestamp":1628121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Deutsches Krebsforschungszentrum (DKFZ)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Object detection and image segmentation of regions of interest provide the foundation for numerous pipelines across disciplines. Robust and accurate computer vision methods are needed to properly solve image-based tasks. Multiple algorithms have been developed to solely detect edges in images. Constrained to the problem of creating a thin, one-pixel wide, edge from a predicted object boundary, we require an algorithm that removes pixels while preserving the topology. Thanks to skeletonize algorithms, an object boundary is transformed into an edge; contrasting uncertainty with exact positions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To extract edges from boundaries generated from different algorithms, we present a computational pipeline that relies on: a novel skeletonize algorithm, a non-exhaustive discrete parameter search to find the optimal parameter combination of a specific post-processing pipeline, and an extensive evaluation using three data sets from the medical and natural image domains (kidney boundaries, NYU-Depth V2, BSDS 500). While the skeletonize algorithm was compared to classical topological skeletons, the validity of our post-processing algorithm was evaluated by integrating the original post-processing methods from six different works.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using the state of the art metrics, precision and recall based Signed Distance Error (SDE) and the Intersection over Union bounding box (IOU-box), our results indicate that the SDE metric for these edges is improved up to 2.3 times.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our work provides guidance for parameter tuning and algorithm selection in the post-processing of predicted object boundaries.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-021-00650-z","type":"journal-article","created":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T03:02:51Z","timestamp":1628132571000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards improving edge quality using combinatorial optimization and a novel skeletonize algorithm"],"prefix":"10.1186","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8772-4305","authenticated-orcid":false,"given":"Marvin","family":"Arnold","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefanie","family":"Speidel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4168-8254","authenticated-orcid":false,"given":"Georges","family":"Hattab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,5]]},"reference":[{"issue":"2","key":"650_CR1","first-page":"104","volume":"3","author":"N Salman","year":"2006","unstructured":"Salman N. Image segmentation based on watershed and edge detection techniques. Int Arab J Inf Technol. 2006;3(2):104\u201310.","journal-title":"Int Arab J Inf Technol"},{"key":"650_CR2","doi-asserted-by":"crossref","unstructured":"Zhan C, Duan X, Xu S, Song Z, Luo M. An improved moving object detection algorithm based on frame difference and edge detection. In: Fourth international conference on image and graphics (ICIG 2007). 2007. p. 519\u2013523.","DOI":"10.1109\/ICIG.2007.153"},{"key":"650_CR3","doi-asserted-by":"crossref","unstructured":"Zitnick CL, Doll\u00e1r P. Edge boxes: locating object proposals from edges. In: European conference on computer vision. Springer; 2014. p. 391\u2013405.","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"650_CR4","doi-asserted-by":"publisher","unstructured":"Hattab G, Meyer F, Albrecht R, Speidel S. Modelar: a modular and evaluative framework to improve surgical augmented reality visualization. In: Kerren A, Garth C, Marai GE, editors. EuroVis 2020: short papers. 2020 https:\/\/doi.org\/10.2312\/evs.20201066.","DOI":"10.2312\/evs.20201066"},{"key":"650_CR5","unstructured":"Roberts LG. Machine perception of three-dimensional solids. Ph.D Thesis, Massachusetts Institute of Technology; 1963."},{"issue":"1","key":"650_CR6","first-page":"15","volume":"10","author":"JM Prewitt","year":"1970","unstructured":"Prewitt JM. Object enhancement and extraction. Pict Process Psychopict. 1970;10(1):15\u20139.","journal-title":"Pict Process Psychopict"},{"key":"650_CR7","unstructured":"Sobel I. Camera models and machine perception. Computer Science Department, Technion: Technical report; 1972."},{"issue":"1167","key":"650_CR8","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1098\/rspb.1980.0020","volume":"207","author":"D Marr","year":"1980","unstructured":"Marr D, Hildreth E. Theory of edge detection. Proc R Soc Lond Ser B Biol Sci. 1980;207(1167):187\u2013217.","journal-title":"Proc R Soc Lond Ser B Biol Sci"},{"key":"650_CR9","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"6","author":"J Canny","year":"1986","unstructured":"Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986;6:679\u201398.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"650_CR10","doi-asserted-by":"crossref","unstructured":"Acuna D, Kar A, Fidler S. Devil is in the edges: learning semantic boundaries from noisy annotations. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2019. p. 11075\u201311083.","DOI":"10.1109\/CVPR.2019.01133"},{"key":"650_CR11","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","volume":"135","author":"D Marmanis","year":"2018","unstructured":"Marmanis D, Schindler K, Wegner JD, Galliani S, Datcu M, Stilla U. Classification with an edge: improving semantic image segmentation with boundary detection. ISPRS J Photogram Remote Sens. 2018;135:158\u201372.","journal-title":"ISPRS J Photogram Remote Sens"},{"key":"650_CR12","doi-asserted-by":"crossref","unstructured":"Wu Z, Li S, Chen C, Hao A, Qin H. A deeper look at image salient object detection: Bi-stream network with a small training dataset. IEEE Trans Multimed. 2020.","DOI":"10.1109\/TMM.2020.3046871"},{"key":"650_CR13","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/TIP.2020.3037470","volume":"30","author":"X Wang","year":"2020","unstructured":"Wang X, Li S, Chen C, Fang Y, Hao A, Qin H. Data-level recombination and lightweight fusion scheme for RGB-D salient object detection. IEEE Trans Image Process. 2020;30:458\u201371.","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"650_CR14","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s11548-019-02102-0","volume":"15","author":"G Hattab","year":"2020","unstructured":"Hattab G, Arnold M, Strenger L, Allan M, Arsentjeva D, Gold O, Simpfend\u00f6rfer T, Maier-Hein L, Speidel S. Kidney edge detection in laparoscopic image data for computer-assisted surgery. Int J Comput Assist Radiol Surg. 2020;15(3):379\u201387.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"4","key":"650_CR15","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1109\/TPAMI.2005.84","volume":"27","author":"S Wang","year":"2005","unstructured":"Wang S, Kubota T, Siskind JM, Wang J. Salient closed boundary extraction with ratio contour. IEEE Trans Pattern Anal Mach Intell. 2005;27(4):546.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"650_CR16","doi-asserted-by":"publisher","first-page":"037004","DOI":"10.1117\/1.2715574","volume":"46","author":"P Rulic","year":"2007","unstructured":"Rulic P, Kramberger I, Kacic Z. Progressive method for color selective edge detection. Opt Eng. 2007;46(3):037004.","journal-title":"Opt Eng"},{"key":"650_CR17","doi-asserted-by":"crossref","unstructured":"Borga M, Malmgren H, Knutsson H. Fsed-feature selective edge detection. In: Proceedings 15th international conference on pattern recognition, ICPR-2000, vol. 1. IEEE; 2000. p. 229\u2013232.","DOI":"10.1109\/ICPR.2000.905309"},{"key":"650_CR18","doi-asserted-by":"crossref","unstructured":"Somandepalli K, Toutios A, Narayanan SS. Semantic edge detection for tracking vocal tract air-tissue boundaries in real-time magnetic resonance images. In: Interspeech. 2017. p. 631\u2013635.","DOI":"10.21437\/Interspeech.2017-1580"},{"key":"650_CR19","doi-asserted-by":"crossref","unstructured":"Qi H, Collins S, Alison\u00a0Noble J. Upi-net: semantic contour detection in placental ultrasound. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops. 2019.","DOI":"10.1109\/ICCVW.2019.00053"},{"issue":"2","key":"650_CR20","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/s10439-011-0441-z","volume":"40","author":"D Stoyanov","year":"2012","unstructured":"Stoyanov D. Surgical vision. Ann Biomed Eng. 2012;40(2):332\u201345.","journal-title":"Ann Biomed Eng"},{"key":"650_CR21","doi-asserted-by":"publisher","first-page":"105771","DOI":"10.1016\/j.cmpb.2020.105771","volume":"198","author":"MC Fiorentino","year":"2021","unstructured":"Fiorentino MC, Moccia S, Capparuccini M, Giamberini S, Frontoni E. A regression framework to head-circumference delineation from us fetal images. Comput Methods Programs Biomed. 2021;198:105771.","journal-title":"Comput Methods Programs Biomed"},{"issue":"3","key":"650_CR22","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1145\/357994.358023","volume":"27","author":"T Zhang","year":"1984","unstructured":"Zhang T, Suen CY. A fast parallel algorithm for thinning digital patterns. Commun ACM. 1984;27(3):236\u20139.","journal-title":"Commun ACM"},{"issue":"6","key":"650_CR23","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1006\/cgip.1994.1042","volume":"56","author":"T-C Lee","year":"1994","unstructured":"Lee T-C, Kashyap RL, Chu C-N. Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP Graph Models Image Process. 1994;56(6):462\u201378.","journal-title":"CVGIP Graph Models Image Process"},{"key":"650_CR24","doi-asserted-by":"crossref","unstructured":"Silberman N, Hoiem D, Kholi P, Fergus R. Indoor segmentation and support inference from RGBD images. In: ECCV 2012.","DOI":"10.1007\/978-3-642-33715-4_54"},{"issue":"5","key":"650_CR25","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","volume":"33","author":"P Arbelaez","year":"2011","unstructured":"Arbelaez P, Maire M, Fowlkes C, Malik J. Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell. 2011;33(5):898\u2013916. https:\/\/doi.org\/10.1109\/TPAMI.2010.161.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"650_CR26","first-page":"676","volume-title":"Digital image processing","author":"RC Gonzalez","year":"2006","unstructured":"Gonzalez RC, Woods RE. Digital image processing. 3rd ed. Upper Saddle River: Prentice-Hall Inc; 2006. p. 676\u20138.","edition":"3"},{"key":"650_CR27","doi-asserted-by":"crossref","unstructured":"Qian K, Cao S, Bhattacharya P. Skeletonization of gray-scale images by gray weighted distance transform. In: Visual information processing VI, vol. 3074. International Society for Optics and Photonics; 1997. p. 224\u2013228.","DOI":"10.1117\/12.280625"},{"issue":"1","key":"650_CR28","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/0146-664X(75)90022-2","volume":"4","author":"S Yokoi","year":"1975","unstructured":"Yokoi S, Toriwaki J-I, Fukumura T. An analysis of topological properties of digitized binary pictures using local features. Comput Graph Image Process. 1975;4(1):63\u201373.","journal-title":"Comput Graph Image Process"},{"key":"650_CR29","unstructured":"Xiaofeng R, Bo L. Discriminatively trained sparse code gradients for contour detection. In: Advances in neural information processing systems. 2012. p. 584\u2013592."},{"key":"650_CR30","unstructured":"Shen W, Wang X, Wang Y, Bai X, Zhang Z. Deepcontour: A deep convolutional feature learned by positive-sharing loss for contour detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. p. 3982\u20133991."},{"key":"650_CR31","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r P, Zitnick CL. Structured forests for fast edge detection. In: Proceedings of the IEEE international conference on computer vision. 2013. p. 1841\u20131848.","DOI":"10.1109\/ICCV.2013.231"},{"key":"650_CR32","doi-asserted-by":"crossref","unstructured":"Liu Y, Cheng M-M, Hu X, Wang K, Bai X. Richer convolutional features for edge detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. p. 3000\u20133009.","DOI":"10.1109\/CVPR.2017.622"},{"key":"650_CR33","doi-asserted-by":"crossref","unstructured":"He J, Zhang S, Yang M, Shan Y, Huang T. Bi-directional cascade network for perceptual edge detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2019. p. 3828\u20133837.","DOI":"10.1109\/CVPR.2019.00395"},{"key":"650_CR34","unstructured":"Everingham M, Van\u00a0Gool L, Williams CKI, Winn J, Zisserman A. The PASCAL visual object classes challenge 2012 (VOC2012) Results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html."},{"key":"650_CR35","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.visres.2015.11.007","volume":"120","author":"DA M\u00e9ly","year":"2016","unstructured":"M\u00e9ly DA, Kim J, McGill M, Guo Y, Serre T. A systematic comparison between visual cues for boundary detection. Vis Res. 2016;120:93\u2013107.","journal-title":"Vis Res"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-021-00650-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-021-00650-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-021-00650-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T17:33:36Z","timestamp":1725557616000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-021-00650-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,5]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["650"],"URL":"https:\/\/doi.org\/10.1186\/s12880-021-00650-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-151023\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,5]]},"assertion":[{"value":"19 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"119"}}