{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:38:36Z","timestamp":1777891116579,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2021,3,25]]},"abstract":"<jats:p>BACKGROUND AND OBJECTIVE: At present, there are many methods for pathological lung segmentation. However, there are still two unresolved problems. (1) The search steps in traditional ASM is a least square optimization method, which is sensitive to outlier marker points, and it makes the profile update to the transition area in the middle of normal lung tissue and tumor rather than a true lung contour. (2) If the noise images exist in the training dataset, the corrected shape model cannot be constructed. METHODS: To solve the first problem, we proposed a new ASM algorithm. Firstly, we detected these outlier marker points by a distance method, and then the different searching functions to the abnormal and normal marker points are applied. To solve the second problem, robust principal component analysis (RPCA) of low rank theory can remove noise, so the proposed method combines RPCA instead of PCA with ASM to solve this problem. Low rank decompose for marker points matrix of training dataset and covariance matrix of PCA will be done before segmentation using ASM. RESULTS: Using the proposed method to segment 122 lung images with juxta-pleural tumors of EMPIRE10 database, got the overlap rate with the gold standard as 94.5%. While the accuracy of ASM based on PCA is only 69.5%. CONCLUSIONS: The results showed that when the noise sample is contained in the training sample set, a good segmentation result for the lungs with juxta-pleural tumors can be obtained by the ASM based on RPCA.<\/jats:p>","DOI":"10.3233\/thc-218037","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T13:13:20Z","timestamp":1614690800000},"page":"385-398","source":"Crossref","is-referenced-by-count":2,"title":["3D segmentation of lungs with juxta-pleural tumor using the improved active shape model approach"],"prefix":"10.1177","volume":"29","author":[{"given":"Shenshen","family":"Sun","sequence":"first","affiliation":[{"name":"College of Information and Engineering, Shenyang University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huizhi","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Dan","sequence":"additional","affiliation":[{"name":"College of Information and Engineering, Shenyang University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Information and Engineering, Shenyang University, Shenyang, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/THC-218037_ref1","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.compbiomed.2018.10.011","article-title":"Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs","volume":"103","author":"Gu","year":"2018","journal-title":"Computers in Biology and Medicine."},{"issue":"5","key":"10.3233\/THC-218037_ref2","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1049\/iet-cvi.2017.0226","article-title":"Automatic lung segmentation based on Graph Cut using a distance-constrained energy","volume":"12","author":"Oluyide","year":"2018","journal-title":"IET Computer Vision."},{"key":"10.3233\/THC-218037_ref3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.patrec.2019.12.006","article-title":"A comprehensive review on multi-organs tumor detection based on machine learning","volume":"131","author":"Sharif","year":"2020","journal-title":"Pattern Recognition Letters."},{"key":"10.3233\/THC-218037_ref4","unstructured":"Sun S, Bauer C, Beichel R. 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IEEE Transactions on Industrial Electronics, 2020.","DOI":"10.1109\/TIE.2020.2975484"},{"issue":"6","key":"10.3233\/THC-218037_ref11","doi-asserted-by":"crossref","first-page":"e0199141","DOI":"10.1371\/journal.pone.0199141","article-title":"Self-supervised sparse coding scheme for image classification based on low rank representation","volume":"13","author":"Li","year":"2018","journal-title":"PloS One."},{"key":"10.3233\/THC-218037_ref12","unstructured":"Hu Z, Nie F, Tian L, et al. A comprehensive survey for low rank regularization. arXiv preprint arXiv1808.04521, 2018."},{"issue":"1","key":"10.3233\/THC-218037_ref13","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.media.2011.08.004","article-title":"Towards robust and effective shape modeling Sparse shape composition","volume":"16","author":"Zhang","year":"2012","journal-title":"Medical Image Analysis."},{"key":"10.3233\/THC-218037_ref14","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.media.2017.02.008","article-title":"Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation","volume":"38","author":"Shi","year":"2017","journal-title":"Medical Image Analysis."},{"issue":"9","key":"10.3233\/THC-218037_ref15","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1109\/TMI.2014.2305691","article-title":"Hierarchical lung field segmentation with joint shape and appearance sparse learning","volume":"33","author":"Shao","year":"2014","journal-title":"IEEE Transactions on Medical Imaging."},{"issue":"6","key":"10.3233\/THC-218037_ref16","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1109\/42.929615","article-title":"Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images","volume":"20","author":"Hu","year":"2001","journal-title":"IEEE Trans Med Imaging."},{"key":"10.3233\/THC-218037_ref17","doi-asserted-by":"crossref","unstructured":"Sun S, Ren H, Meng F. 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Imaging"},{"issue":"3","key":"10.3233\/THC-218037_ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust principal component analysis","volume":"58","author":"Cand\u00e8s","year":"2011","journal-title":"Journal of the ACM (JACM)."}],"container-title":["Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/THC-218037","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:54:34Z","timestamp":1777614874000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/THC-218037"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":20,"URL":"https:\/\/doi.org\/10.3233\/thc-218037","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]}}}