乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T15:12:06Z","timestamp":1744211526698},"reference-count":52,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2021,3]]},"abstract":"Abstract<\/jats:title>The novel coronavirus disease (SARS\u2010CoV\u20102 or COVID\u201019) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID\u201019 detection. However, lung infection by COVID\u201019 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID\u201019 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region\u2010specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co\u2010occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID\u201019 infection. The proposed algorithm was compared with other existing state\u2010of\u2010the\u2010art deep neural networks using the Radiopedia and COVID\u201019 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance\u2010alignment measure (EM\u03c6<\/jats:sub>), and structure measure (S<\/jats:italic>m<\/jats:sub>) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID\u201019 infection with limited datasets.<\/jats:p>","DOI":"10.1002\/ima.22525","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T06:30:17Z","timestamp":1606199417000},"page":"28-46","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An integrated feature frame work for automated segmentation of COVID<\/scp>\u201019 infection from lung CT images<\/scp>"],"prefix":"10.1002","volume":"31","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0348-6627","authenticated-orcid":false,"given":"Deepika","family":"Selvaraj","sequence":"first","affiliation":[{"name":"Department of Micro and Nano Electronics, School of Electronics Engineering Vellore Institute of Technology Vellore India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7797-4749","authenticated-orcid":false,"given":"Arunachalam","family":"Venkatesan","sequence":"additional","affiliation":[{"name":"Department of Micro and Nano Electronics, School of Electronics Engineering Vellore Institute of Technology Vellore India"}]},{"given":"Vijayalakshmi G. V.","family":"Mahesh","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication BMS Institute of Technology and Management Bangalore India"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1505-3159","authenticated-orcid":false,"given":"Alex Noel","family":"Joseph Raj","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Department of Electronic Engineering College of Engineering, Shantou University Shantou China"}]}],"member":"311","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"e_1_2_9_2_1","first-page":"1","article-title":"Review of Artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID\u201019","author":"Shi F","year":"2020","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.139652"},{"key":"e_1_2_9_4_1","volume-title":"Artificial Intelligence for Coronavirus Outbreak","author":"Fong SJ","year":"2020"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.01303-2016"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200343"},{"key":"e_1_2_9_8_1","unstructured":"Lee JH Kim DI Cho MK.Computed tomography apparatus and method of controlling X\u2010ray by using the same ed: Google Patents 2017.https:\/\/patents.google.com\/patent\/US9655584B2\/en"},{"key":"e_1_2_9_9_1","unstructured":"Forthmann P Pfleiderer G.Augmented display device for use in a medical imaging laboratory.Google Patents 2019."},{"key":"e_1_2_9_10_1","unstructured":"Jensen VT.Method and system of acquiring images with a medical imaging device ed: Google Patents 2009.https:\/\/patents.google.com\/patent\/US20060293582"},{"key":"e_1_2_9_11_1","unstructured":"Scheib S.Dosimetric end\u2010to\u2010end verification devices systems and methods. U.S. Patent 9 643 029 issued May 9 2017.https:\/\/patents.google.com\/patent\/US20150085993"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.20.22954"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.20.23034"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30183-5"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijantimicag.2020.105948"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-06817-6"},{"key":"e_1_2_9_17_1","article-title":"Harmony\u2010search and otsu based system for coronavirus disease (COVID\u201019) detection using lung CT scan images","author":"Rajinikanth V","year":"2020","journal-title":"arXiv preprint arXiv:2004.03431"},{"key":"e_1_2_9_18_1","unstructured":"https:\/\/radiopaedia.org\/articles\/COVID-19-3?lang=us.LastAccessed date April 5 2020."},{"key":"e_1_2_9_19_1","unstructured":"Ga\u00e1lG MagaB Luk\u00e1csA.Attention u\u2010net based adversarial architectures for chest x\u2010ray lung segmentation.arXiv preprint arXiv:2003.10304;2020.https:\/\/arxiv.org\/pdf\/2003.10304.pdf"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_22_1","unstructured":"OktayO SchlemperJ FolgocLL LeeM HeinrichM MisawaK GlockerB.Attention u\u2010net: Learning where to look for the pancreas.arXiv preprint arXiv:1804.03999;2018.https:\/\/arxiv.org\/pdf\/1804.03999.pdf"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.012"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"e_1_2_9_26_1","unstructured":"Fu H Fan DP Chen G Zhou T.COVID\u201019 imaging\u2010based AI research collection. 2020.https:\/\/github.com\/HzFu\/COVID19_imaging_AI_paper_list."},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22211"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.4018\/IJACI.2019070106"},{"key":"e_1_2_9_30_1","unstructured":"ShanF GaoY WangJ. ShiW ShiN HanM ShiY.Lung infection quantification of covid\u201019 in CT images with deep learning.arXiv preprint arXiv:2003.04655;2020.https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2003\/2003.04655.pdf"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899635"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.01.055"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13458"},{"key":"e_1_2_9_34_1","unstructured":"ChenX YaoL ZhangY.Residual attention U\u2010net for automated multi\u2010class segmentation of COVID\u201019 chest CT images.arXiv preprint arXiv:2004.05645;2020.https:\/\/arxiv.org\/pdf\/2004.05645.pdf"},{"key":"e_1_2_9_35_1","doi-asserted-by":"crossref","unstructured":"ChenX YaoL ZhouT DongJ ZhangY.Momentum contrastive learning for few\u2010shot covid\u201019 diagnosis from chest CT images.arXiv preprint arXiv:2006.13276;2020.https:\/\/arxiv.org\/pdf\/2006.13276.pdf","DOI":"10.1016\/j.patcog.2021.107826"},{"key":"e_1_2_9_36_1","unstructured":"NarinA KayaC PamukZ.Automatic detection of coronavirus disease (covid\u201019) using x\u2010ray images and deep convolutional neural networks.arXiv preprint arXiv:2003.10849;2020.https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2003\/2003.10849.pdf"},{"key":"e_1_2_9_37_1","unstructured":"ShiF XiaL ShanF WuD WeiY YuanH ShenD.Large\u2010scale screening of covid\u201019 from community acquired pneumonia using infection size\u2010aware classification.arXiv preprint arXiv:2003.09860;2020.https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2003\/2003.09860.pdf"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2993291"},{"key":"e_1_2_9_39_1","doi-asserted-by":"crossref","unstructured":"WangL WongA.COVID\u2010Net: a tailored deep convolutional neural network Design for Detection of COVID\u201019 cases from chest X\u2010ray images.arXiv preprint arXiv:2003.09871;2020.https:\/\/arxiv.org\/pdf\/2003.09871.pdf","DOI":"10.1038\/s41598-020-76550-z"},{"key":"e_1_2_9_40_1","first-page":"1","article-title":"RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia","author":"Butt C","year":"2020","journal-title":"Applied Intelligence"},{"key":"e_1_2_9_41_1","article-title":"Auto\u2010Diagnosis of COVID\u201019 Using Lung CT Images with Semi\u2010Supervised Shallow Learning Network","author":"Konar D","year":"2020","journal-title":"Research Square"},{"key":"e_1_2_9_42_1","article-title":"Transfer Learning Based Ensemble Support Vector Machine Model for Automated COVID\u201019 Detection Using Lung Computerized Tomography Scan Data","author":"Singh M","year":"2020","journal-title":"Research Square"},{"key":"e_1_2_9_43_1","article-title":"Deep transfer learning\u2010based automated detection of COVID\u201019 from lung CT scan slices","author":"Ahuja S","year":"2020","journal-title":"Applied Intelligence"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-020-09751-3"},{"key":"e_1_2_9_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2995965"},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780429441134"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22272"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-5225-2053-5.ch004"},{"key":"e_1_2_9_49_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0229831"},{"key":"e_1_2_9_50_1","unstructured":"BarstuganM OzkayaU OzturkS.Coronavirus (covid\u201019) classification using ct images by machine learning methods.arXiv preprint arXiv:2003.09424;2020.https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2003\/2003.09424.pdf"},{"key":"e_1_2_9_51_1","unstructured":"COVID\u201019 CT segmentation dataset.https:\/\/medicalsegmentation.com\/covid19\/.AccessedApril 11 2020."},{"key":"e_1_2_9_52_1","unstructured":"CohenJP MorrisonP DaoL RothK DuongTQ GhassemiM.Covid\u201019 image data collection: Prospective predictions are the future.arXiv preprint arXiv:2006.11988;2020.https:\/\/arxiv.org\/pdf\/2006.11988.pdf"},{"key":"e_1_2_9_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.487"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.22525","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ima.22525","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.22525","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T01:24:11Z","timestamp":1693704251000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.22525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["10.1002\/ima.22525"],"URL":"https:\/\/doi.org\/10.1002\/ima.22525","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"value":"0899-9457","type":"print"},{"value":"1098-1098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,23]]},"assertion":[{"value":"2020-08-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-10-31","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}