{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T11:14:51Z","timestamp":1651835691505},"reference-count":38,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>A radiologist must read hundreds of slices to recognize a malignant or benign lung tumor in computed tomography (CT) volume data. To reduce the burden of the radiologist, some proposals have been applied with the ground-glass opacity (GGO) nodules. However, the GGO nodules need be detected and labeled by a radiologist manually. Some slices with the GGO nodule can be missed because there are many slices in several volume data. Although some papers have proposed a semi-supervised learning method to find the slices with GGO nodules, the was no discussion on the impact of parameters in the proposed semi-supervised learning. This article also explains and analyzes the label propagation algorithm which is one of the semi-supervised learning methods to detect the slices including the GGO nodules based on the parameters. Experimental results show that the proposal can detect the slices including the GGO nodules effectively.<\/jats:p>","DOI":"10.4018\/ijsi.2019010106","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T11:50:16Z","timestamp":1540986616000},"page":"104-118","source":"Crossref","is-referenced-by-count":1,"title":["Label Propagation Algorithm for the Slices Detection of a Ground-Glass Opacity Nodule"],"prefix":"10.4018","volume":"7","author":[{"given":"Weiwei","family":"Du","sequence":"first","affiliation":[{"name":"Information and Human Science, Kyoto Institute of Technology, Kyoto, Japan"}]},{"given":"Dandan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China"}]},{"given":"Jianming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China"}]},{"given":"Xiaojie","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China"}]},{"given":"Yanhe","family":"Ma","sequence":"additional","affiliation":[{"name":"Tianjin Chest Hospital, Tianjin, China"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Chest Hospital, Tianjin, China"}]}],"member":"2432","reference":[{"key":"IJSI.2019010106-0","author":"A. J.Alberg","year":"2016","journal-title":"Epidemiology of lung cancer. In Murray & Nadel\u2019s Textbook of Respiratory Medicine"},{"key":"IJSI.2019010106-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2004.02.009"},{"key":"IJSI.2019010106-2","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D. & Jemal, A. (2016). Cancer Statistics. CA: a Cancer Journal for Clinicians, 66(1), 7\u201330.26742998","DOI":"10.3322\/caac.21332"},{"key":"IJSI.2019010106-3","author":"R. H.Carmona","year":"2006","journal-title":"The Health Consequences of Involuntary Exposure to Tobacco Smoke: A Report of the Surgeon General"},{"key":"IJSI.2019010106-4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.14132187"},{"key":"IJSI.2019010106-5","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21338"},{"key":"IJSI.2019010106-6","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"IJSI.2019010106-7","doi-asserted-by":"publisher","DOI":"10.1093\/ietisy\/e89-d.7.2315"},{"key":"IJSI.2019010106-8","doi-asserted-by":"crossref","unstructured":"Du, W., Liu, Y. P., Wang, S. Y., Peng, Y. H., & Oto, A. (2016), Features extraction of prostate with graph spectral method for prostate cancer detection. In Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing (SNPD).","DOI":"10.1109\/SNPD.2016.7515975"},{"key":"IJSI.2019010106-9","doi-asserted-by":"publisher","DOI":"10.1093\/ietisy\/e90-d.9.1456"},{"issue":"2","key":"IJSI.2019010106-10","first-page":"442","article-title":"Natural image matting with membership propagation","volume":"4","author":"W.Du","year":"2009","journal-title":"Information and Media Technologies"},{"key":"IJSI.2019010106-11","doi-asserted-by":"crossref","unstructured":"Du, W., Wang, J., Yuan, D., Miao, Y., Ma, Y., & Zhang, H. (2017). Development of an interface for features extraction of a ground-glass opacity nodule.","DOI":"10.1007\/978-3-319-60170-0_16"},{"key":"IJSI.2019010106-12","unstructured":"Du, W., Wang, S., Oto, A., & Peng, Y. (2015). Graph-based prostrate extraction in T2-weighted images for prostate cancer detection. In The 11th International Conference on Natural Computation and The 12th International Conference Fuzzy System and Knowledge Discovery."},{"key":"IJSI.2019010106-13","first-page":"207","article-title":"Development of an interface for volumetric measurement on a ground-glass opacity nodule.","author":"W.Du","year":"2017","journal-title":"International Conference on Computer and Information Science"},{"key":"IJSI.2019010106-14","doi-asserted-by":"publisher","DOI":"10.1109\/IntelliSys.2015.7361223"},{"key":"IJSI.2019010106-15","doi-asserted-by":"publisher","DOI":"10.1109\/IntelliSys.2015.7361223"},{"key":"IJSI.2019010106-16","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2014.2328870"},{"key":"IJSI.2019010106-17","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2462070712"},{"key":"IJSI.2019010106-18","unstructured":"He, Y., Yu, H., & Wang, G. (2016). Deep Learning for the Classification of Lung Nodules. arXiv:1611.06651"},{"key":"IJSI.2019010106-19","unstructured":"Inoue, K., & Urahama, K. (2005). Robust extraction of arbitrarily-shaped clusters using graph-spectral method with regularized normalization. In FIT\u201905 (in Japanese)"},{"key":"IJSI.2019010106-20","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2725903"},{"key":"IJSI.2019010106-21","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2372041887"},{"key":"IJSI.2019010106-22","doi-asserted-by":"crossref","unstructured":"Miao, Y., Wang, J., Du, W., Ma, Y., & Zhang, H. (2016). Volumetric Measurement of Ground-Glass Opacity Nodules using Expectation-Maximization Algorithm. In The 4th IIAE International Conference on Intelligent Systems and Image Processing 2016: The Institute of Industrial Applications Engineers (pp. 317-321).","DOI":"10.12792\/icisip2016.056"},{"key":"IJSI.2019010106-23","unstructured":"Miao, Y., Wang, J., Du, W., Ma, Y., & Zhang, H. (2017). Feature extraction of ground-glass opacity nodules using active contour model for lung cancer detection. Advances in Computer Science Research, 71, 1312-1317."},{"key":"IJSI.2019010106-24","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.214.3.r00mr22823"},{"issue":"8","key":"IJSI.2019010106-25","first-page":"888","article-title":"On spectral clustering: Analysis and an algorithm","volume":"22","author":"M. I. J. A. Y.Ng","year":"2000","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"IJSI.2019010106-26","first-page":"683","article-title":"Asbestos-related lung disease.","volume":"75","author":"K. M.O\u2019Reilly","year":"2007","journal-title":"American Family Physician"},{"issue":"2","key":"IJSI.2019010106-27","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1148\/rg.272065061","article-title":"Nodular ground-glass opacity at thin-section CT: Histologic correlation and evaluation of change at follow-up.","volume":"27","author":"C. M.Park","year":"2007","journal-title":"Radiographics"},{"key":"IJSI.2019010106-28","unstructured":"Philips Healthcare. (2013). DICOM Conformance Statement for DICOM Viewer Release 3.0."},{"key":"IJSI.2019010106-29","first-page":"97","article-title":"Why should i trust you?: Explaining the predictions of any classifier.","author":"M. T.Ribeiro","year":"2016","journal-title":"Data Mining and Knowledge Discovery"},{"key":"IJSI.2019010106-30","doi-asserted-by":"publisher","DOI":"10.1118\/1.1580485"},{"key":"IJSI.2019010106-31","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.852048"},{"key":"IJSI.2019010106-32","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.180.3.1800817"},{"key":"IJSI.2019010106-33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ypmed.2016.04.015"},{"key":"IJSI.2019010106-34","doi-asserted-by":"crossref","unstructured":"Yaguchi, A., Okazaki, T., Takeguchi, T., Matsumoto, S., Ohno, Y., Aoyagi, K., & Yamagata, H. (2015). Semi-Automated Segmentation of Solid and GGO Nodules in Lung CT Images using Vessel-Likelihood Derived from Local Foreground Structure. Proc. of SPIE (pp. 742-746).","DOI":"10.1117\/12.2080928"},{"key":"IJSI.2019010106-35","doi-asserted-by":"publisher","DOI":"10.1109\/SNPD.2017.8022778"},{"key":"IJSI.2019010106-36","doi-asserted-by":"crossref","unstructured":"Zhou, D., & Scholkopf, B. (2004). Learning from labeled and unlabeled data using random walks. In DAGM- Symposium (pp. 237-244).","DOI":"10.1007\/978-3-540-28649-3_29"},{"key":"IJSI.2019010106-37","first-page":"912","article-title":"Semi-supervised learning using gaussian fields and harmonic functions.","author":"X.Zhu","year":"2003","journal-title":"Proceedings of the 20th International conference on Machine learning (ICML-03)"}],"container-title":["International Journal of Software Innovation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=217395","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T10:46:22Z","timestamp":1651833982000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSI.2019010106"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":38,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.4018\/ijsi.2019010106","relation":{},"ISSN":["2166-7160","2166-7179"],"issn-type":[{"value":"2166-7160","type":"print"},{"value":"2166-7179","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1]]}}}