{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:54:17Z","timestamp":1753887257870,"version":"3.41.2"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":154,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014902","name":"Jiamusi University","doi-asserted-by":"publisher","award":["L2012-075"],"award-info":[{"award-number":["L2012-075"]}],"id":[{"id":"10.13039\/501100014902","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford\u2010Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.<\/jats:p>","DOI":"10.1155\/2021\/2906868","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T21:36:32Z","timestamp":1622842592000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour"],"prefix":"10.1155","volume":"2021","author":[{"given":"Yuntao","family":"Wei","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3537-4179","authenticated-orcid":false,"given":"Xiaojuan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.14257\/ijca.2017.10.4.24"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.14257\/ijfgcn.2017.10.2.03"},{"key":"e_1_2_11_3_2","first-page":"1","article-title":"Segmentation of images using two parameter logistic type distribution and K-means clustering","volume":"11","author":"Srinivasa Rao K.","year":"2018","journal-title":"International Journal of Grid and Distributed Computing"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-020-02199-5"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-28100-x"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3039540"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14127"},{"key":"e_1_2_11_8_2","first-page":"1","article-title":"White blood cell segmentation in Giemsa-stained images of blood smears","volume":"140","author":"Hamghalam M.","year":"2020","journal-title":"International Journal of Advanced Science and Technology"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.33832\/ijast.2019.133.03"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-015-0068-x"},{"key":"e_1_2_11_11_2","first-page":"229","article-title":"Image semantic segmentation based on convolutional neural network features and improved superpixel matching","volume":"55","author":"Chengcheng G.","year":"2018","journal-title":"Progress in Laser and Optoelectronics"},{"key":"e_1_2_11_12_2","first-page":"419","article-title":"Image copy-paste tampering detection based on superpixel segmentation","volume":"37","author":"Jiarui L.","year":"2019","journal-title":"Journal of Applied Sciences"},{"key":"e_1_2_11_13_2","first-page":"83","article-title":"A new method of image superpixel segmentation","volume":"42","author":"Miao L.","year":"2020","journal-title":"Journal of Electronics and Information Technology"},{"key":"e_1_2_11_14_2","first-page":"1","article-title":"CT image segmentation method combining superpixels and CNN","volume":"56","author":"Yongpeng T.","year":"2019","journal-title":"Computer Engineering and Applications"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.12086\/oee.2020.190104"},{"key":"e_1_2_11_16_2","first-page":"137","article-title":"Lung parenchymal CT image refinement segmentation","volume":"22","author":"Yan Q.","year":"2017","journal-title":"Journal of Image and Graphics"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.09.002"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2018.5439"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2019.0255"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.14257\/ijast.2018.112.03"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2017.10.008"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3039500"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2020.3027681"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1243-x"},{"key":"e_1_2_11_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2015.2409119"},{"key":"e_1_2_11_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2959609"},{"key":"e_1_2_11_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0052-4"},{"key":"e_1_2_11_28_2","doi-asserted-by":"crossref","unstructured":"BoboM. 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