{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T18:27:44Z","timestamp":1788805664217,"version":"build-2803163510"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2022,1,7]]},"abstract":"<jats:p>The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference (<jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>P<\/a:mi>\n                        <a:mo>&lt;<\/a:mo>\n                        <a:mn>0.05<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula>). The average gradient, SD, and image entropy of the multispectral image assisted by the tissue classification algorithm were 2.0765, 65.2579, and 4.974, respectively, showing statistical difference (<jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>P<\/c:mi>\n                        <c:mo>&lt;<\/c:mo>\n                        <c:mn>0.05<\/c:mn>\n                     <\/c:math>\n                  <\/jats:inline-formula>). Compared with the three reported indicators, the values of the algorithm in this study were higher. The sensitivity and specificity of the multispectral image with the tissue classification algorithm were 85.3% and 70.8%, respectively, which were both greater than those of the image without the algorithm. It showed that the multispectral image assisted by tissue classification algorithm can effectively screen the cervical cancer and can quickly, efficiently, and safely segment the cervical tissue from the lesion area and the nonlesion area. The segmentation result was the same as that of the doctor's disease examination, indicating that it showed high clinical application value. This provided an effective reference for the clinical application of multispectral imaging technology assisted by tissue classification algorithm in the early screening and diagnosis of cervical cancer.<\/jats:p>","DOI":"10.1155\/2022\/9048123","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T16:50:58Z","timestamp":1641574258000},"page":"1-9","source":"Crossref","is-referenced-by-count":3,"title":["Multispectral Image under Tissue Classification Algorithm in Screening of Cervical Cancer"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3409-8571","authenticated-orcid":true,"given":"Pei","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Gynaecology, Hebei Provincial People\u2019s Hospital, Shijiazhuang 050051, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4185-4785","authenticated-orcid":true,"given":"Shuwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Gynaecology, Chengde Hospital of Traditional Chinese Medicine, Chengde 067000, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8093-8474","authenticated-orcid":true,"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Gynaecology, Hebei Provincial People\u2019s Hospital, Shijiazhuang 050051, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6234-3661","authenticated-orcid":true,"given":"Xiaoyan","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Gynaecology, Hebei Provincial People\u2019s Hospital, Shijiazhuang 050051, Hebei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamda.2020.10.023"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2017.05.163"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.soncn.2019.02.003"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-015-1183-4"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-016-0491-5"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.1501"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000020787"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giaa026"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2016.05.022"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1002\/cpcb.46"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1177\/0194599820941013"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1364\/OE.27.035485"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1097\/PRS.0000000000006405"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.3791\/55637"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.3390\/s17051165"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34461-0_46"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1111\/jog.13101"},{"issue":"2","key":"18","first-page":"545","article-title":"The study on combined screening method for TBS and cervical cancer cell\u2019s DNA quantitative analysis based on multi-spectral imaging technology","volume":"37","author":"J. 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