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dc.contributor.authorContreras Ojeda, Sara
dc.contributor.authorDomínguez Jiménez, Juan Antonio
dc.contributor.authorContreras Ortiz, Sonia Helena
dc.date.accessioned2021-02-08T15:52:52Z
dc.date.available2021-02-08T15:52:52Z
dc.date.issued2020-11-03
dc.date.submitted2021-02-03
dc.identifier.citationS. L. Contreras-Ojeda, J. A. Dominguez-Jiménez, and S. H. Contreras-Ortiz "Analysis and classification of lung and muscular tissues in ultrasound images using 2D wavelet transform and machine learning", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830F (3 November 2020); https://doi.org/10.1117/12.2576368spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/9949
dc.description.abstractUltrasound has been considered a safe and accurate alternative to radiography and computerized tomography to diagnose lung diseases such as pneumonia. However, speckle noise, artifacts or certain conditions can difficult image interpretation. For example, in some cases, the pleura line cannot be observed. This work proposes an approach for discriminating between lung and muscular tissues in ultrasound images. We evaluated the symlet and daubechies wavelets for feature extraction, principal component analysis and recursive backward elimination for feature selection, and supervised learning methods for classification. Statistical moments and the energy of the second horizontal coefficient and peak-to-peak root mean squared ratio were the features more outstanding over the rest. The best model was obtained with recursive backward elimination for feature selection and knearest neighbor for classification. Tissue classification was possible with a mean accuracy of 97.5% and area under the curve of 99%. These results offer great insights on the recognition of lung and muscular tissues, which could improve the effectiveness of automatic segmentation and analysis algorithms.spa
dc.format.extent7 páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.sourceProceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830F (2020)spa
dc.titleAnalysis and classification of lung and muscular tissues in ultrasound images using 2D wavelet transform and machine learningspa
dcterms.bibliographicCitationWielpütz, M.O., Heußel, C.P., Herth, F.J.F., Kauczor, H.-U. Radiological diagnosis in lung disease: Factoring treatment options into the choice of diagnostic modality (Open Access) (2014) Deutsches Arzteblatt International, 111 (11), pp. 181-187. Cited 26 times. http://www.aerzteblatt.de/pdf.asp?id=156439 doi: 10.3238/arztebl.2014.0181spa
dcterms.bibliographicCitationReissig, A., Copetti, R., Mathis, G., Mempel, C., Schuler, A., Zechner, P., Aliberti, S., (...), Hoyer, H. Lung ultrasound in the diagnosis and follow-up of community-acquired pneumonia: A prospective, multicenter, diagnostic accuracy study (Open Access) (2012) Chest, 142 (4), pp. 965-972. Cited 236 times. http://journal.publications.chestnet.org/data/Journals/CHEST/25163/chest_142_4_965.pdf doi: 10.1378/chest.12-0364spa
dcterms.bibliographicCitationSoldati, G., Smargiassi, A., Inchingolo, R., Buonsenso, D., Perrone, T., Briganti, D.F., Perlini, S., (...), Demi, L. Proposal for International Standardization of the Use of Lung Ultrasound for Patients With COVID-19 (Open Access) (2020) Journal of Ultrasound in Medicine, 39 (7), pp. 1413-1419. Cited 137 times. http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1550-9613 doi: 10.1002/jum.15285spa
dcterms.bibliographicCitationBuonsenso, D., Raffaelli, F., Tamburrini, E., Biasucci, D.G., Salvi, S., Smargiassi, A., Inchingolo, R., (...), Moro, F. Clinical role of lung ultrasound for diagnosis and monitoring of COVID-19 pneumonia in pregnant women (Open Access) (2020) Ultrasound in Obstetrics and Gynecology, 56 (1), pp. 106-109. Cited 44 times. http://obgyn.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1469-0705/ doi: 10.1002/uog.22055spa
dcterms.bibliographicCitationZenteno, O., Castaneda, B., Lavarello, R. Spectral-based pneumonia detection tool using ultrasound data from pediatric populations (2016) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016-October, art. no. 7591635, pp. 4129-4132. Cited 7 times. ISBN: 978-145770220-4 doi: 10.1109/EMBC.2016.7591635spa
dcterms.bibliographicCitationOrganization, W.H. (2019) Pneumonia. Cited 16 times. Last accessed 18 September 2019spa
dcterms.bibliographicCitationDu, R.-H., Liang, L.-R., Yang, C.-Q., Wang, W., Cao, T.-Z., Li, M., Guo, G.-Y., (...), Shi, H.-Z. Predictors of mortality for patients with COVID-19 pneumonia caused by SARSCoV- 2: A prospective cohort study (Open Access) (2020) European Respiratory Journal, 55 (5), art. no. e2000524. Cited 277 times. https://erj.ersjournals.com/content/erj/55/5/2000524.full.pdf doi: 10.1183/13993003.00524-2020spa
dcterms.bibliographicCitationichtenstein, D.A. Ultrasound examination of the lungs in the intensive care unit (2009) Pediatric Critical Care Medicine, 10 (6), pp. 693-698. Cited 83 times. http://journals.lww.com/pccmjournal doi: 10.1097/PCC.0b013e3181b7f637spa
dcterms.bibliographicCitationCorrea, M., Zimic, M., Barrientos, F., Barrientos, R., Román-Gonzalez, A., Pajuelo, M.J., Anticona, C., (...), Oberhelman, R. Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition (Open Access) (2018) PLoS ONE, 13 (12), art. no. e0206410. Cited 18 times. https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0206410&type=printable doi: 10.1371/journal.pone.0206410spa
dcterms.bibliographicCitationEtehadtavakol, M., Ng, E.Y.K., Chandran, V., Rabbani, H. Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms (2013) Infrared Physics and Technology, 61, pp. 274-286. Cited 45 times. doi: 10.1016/j.infrared.2013.08.009spa
dcterms.bibliographicCitationContreras-Ojeda, S.L., Sierra-Pardo, C., Dominguez-Jimenez, J.A., Lopez-Bueno, J., Contreras-Ortiz, S.H. Texture Analysis of Ultrasound Images for Pneumonia Detection in Pediatric Patients (2019) 2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings, art. no. 8730238. Cited 4 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8719845 ISBN: 978-172811491-0 doi: 10.1109/STSIVA.2019.8730238spa
dcterms.bibliographicCitationValdes-Burgos, L., Contreras-Ojeda, S.L., Domínguez-Jiménez, J.A., López-Bueno, J., Contreras-Ortiz, S.H. Analysis and classification of lung tissue in ultrasound images for pneumonia detection (2020) Proceedings of SPIE - The International Society for Optical Engineering, 11330, art. no. 1133003. http://spie.org/x1848.xml ISBN: 978-151063427-5 doi: 10.1117/12.2542615spa
dcterms.bibliographicCitationYelampalli, P.K.R., Nayak, J., Gaidhane, V.H. Daubechies wavelet-based local feature descriptor for multimodal medical image registration (2018) IET Image Processing, 12 (10), pp. 1692-1702. Cited 12 times. www.ietdl.org/IET-IPR doi: 10.1049/iet-ipr.2017.1305spa
dcterms.bibliographicCitationLayek, K., Samanta, S., Sadhu, A., Maity, S.P., Barui, A. Classification of sonoelastography images of prostate cancer using transformation- based feature extraction techniques (2018) Soft Computing Based Medical Image Analysis, pp. 245-269. Cited 3 times. https://sciencedirect.utb.elogim.com/book/9780128130872/soft-computing-based-medical-image-analysis#book-description ISBN: 978-012813087-2 doi: 10.1016/B978-0-12-813087-2.00013-0spa
dcterms.bibliographicCitationAkkasaligar, P.T., Biradar, S. Diagnosis of renal calculus disease in medical ultrasound images (2016) 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, art. no. 7919642. Cited 6 times. ISBN: 978-150900611-3 doi: 10.1109/ICCIC.2016.7919642spa
dcterms.bibliographicCitationLever, J., Krzywinski, M., Altman, N. (2017) Points of Significance: Principal Component Analysis. Cited 7 times.spa
dcterms.bibliographicCitationLi, F., Yang, Y. Analysis of recursive feature elimination methods (2005) SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 633-634. Cited 5 times. ISBN: 1595930345; 978-159593034-7 doi: 10.1145/1076034.1076164spa
datacite.rightshttp://purl.org/coar/access_right/c_14cbspa
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dc.identifier.urlhttps://www.spiedigitallibrary.org/conference-proceedings-of-spie/11583/0000/Analysis-and-classification-of-lung-and-muscular-tissues-in-ultrasound/10.1117/12.2576368.short
dc.type.driverinfo:eu-repo/semantics/lecturespa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa
dc.identifier.doi10.1117/12.2576368
dc.subject.keywordsBioinformaticsspa
dc.subject.keywordsBiological organsspa
dc.subject.keywordsComputerized tomographyspa
dc.subject.keywordsDiscrete wavelet transformsspa
dc.subject.keywordsFeature extractionspa
dc.subject.keywordsHistologyspa
dc.subject.keywordsImage classificationspa
dc.subject.keywordsMachine learningspa
dc.subject.keywordsNearest neighbor searchspa
dc.subject.keywordsTissuespa
dc.subject.keywordsUltrasonicsspa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.subject.armarcLEMB
dc.type.spahttp://purl.org/coar/resource_type/c_8544spa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_c94fspa


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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.