{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:05:14Z","timestamp":1790831114480,"version":"4.1.0"},"reference-count":43,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2019,12,29]],"date-time":"2019-12-29T00:00:00Z","timestamp":1577577600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41631175"],"award-info":[{"award-number":["41631175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702068"],"award-info":[{"award-number":["61702068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,2]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Several negative factors, such as juxta\u2010pleural nodules, pulmonary vessels, and image noise, make accurately segmenting lungs from computed tomography (CT) images a complex task. We propose a novel hybrid automated algorithm in the paper based on random forest to deal with the issues. Our method aims to eliminate the effect of the factors and generate accurate segmentation of lungs from CT images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our algorithm consists of five main steps: image preprocessing, lung region extraction, trachea elimination, lung separation, and contour correction. A lung CT image is first preprocessed with a novel normal vector correlation\u2010based image denoising approach and decomposed into a group of multiscale subimages. A modified superpixel segmentation method is then performed on the first\u2010level subimage to generate a set of superpixels, and a random forest classifier is employed to segment the lungs by classifying the superpixels of each subimage\u2010based on the features extracted from them. The initial lung segmentation result is further refined through trachea elimination using an iterative thresholding approach, lung separation based on context information of image sequence, and contour correction with a corner detection technique.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our algorithm is tested on a set of CT images affected with interstitial lung diseases, and experiments show that the algorithm achieves high accuracy on lung segmentation with 0.9638 Jaccard\u2019s index and 0.9867 Dice similarity coefficient, compared with ground truths. Additionally, our algorithm achieves an average 7.7% better Dice similarity coefficient than compared conventional lung segmentation methods and 1% better than Deep Learning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our algorithm can segment lungs from lung CT images with good performance in a fully automatic fashion, and it is of great assistance for lung disease detection in the computer\u2010aided detection system.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13939","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T20:57:17Z","timestamp":1575320237000},"page":"518-529","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A fully automatic segmentation algorithm for CT lung images based on random forest"],"prefix":"10.1002","volume":"47","author":[{"given":"Caixia","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of EduInfo Science &amp; Engineering Nanjing Normal University Jiangsu China"},{"name":"Department of Information Science and Engineering Zaozhuang University Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruibin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of EduInfo Science &amp; Engineering Nanjing Normal University Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyong","family":"Pang","sequence":"additional","affiliation":[{"name":"Institute of EduInfo Science &amp; Engineering Nanjing Normal University Jiangsu China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,12,29]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-009-9229-1"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2011.10.001"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.862753"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIMA.2007.236"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2014.03.010"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3147146"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.13005\/bpj\/1315"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2014.07.007"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2004.02.009"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JTEHM.2018.2837901"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2890510"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0058-y"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0052-4"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0164522"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0182678"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4955601"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2004.06.005"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.07.031"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"KaulC ManandharS PearsN.FocusNet: An attention\u2010based Fully Convolutional Network for Medical Image Segmentation arXiv preprint arXiv:1902.03091 1\u20105;2019.","DOI":"10.1109\/ISBI.2019.8759477"},{"key":"e_1_2_8_21_1","first-page":"1","article-title":"Lung segmentation on HRCT and volumetric CT for diffuse interstitial lung disease using deep convolutional neural networks","volume":"2019","author":"Park B","journal-title":"J Dig Imaging"},{"key":"e_1_2_8_22_1","unstructured":"LaLondeR BagciU.Capsules for object segmentation arXiv preprint arXiv:1804.04241 1\u20109;2018."},{"key":"e_1_2_8_23_1","doi-asserted-by":"crossref","unstructured":"HarrisonAP XuZ GeorgeK LuL SummersRM MolluraDJ.Progressive and multi\u2010path holistically nested neural networks for pathological lung segmentation from CT images. 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