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Region growing segmentation of ultrasound images using gradients and local statistics

dc.contributor.editorDuric N.
dc.contributor.editorHeyde B.
dc.creatorMercado-Aguirre I.M.
dc.creatorPatiño Vanegas, Alberto
dc.creatorContreras Ortiz, Sonia Helena
dc.date.accessioned2020-03-26T16:32:39Z
dc.date.available2020-03-26T16:32:39Z
dc.date.issued2017
dc.identifier.citationProgress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10139
dc.identifier.isbn9781510607231
dc.identifier.issn16057422
dc.identifier.urihttps://hdl.handle.net/20.500.12585/8952
dc.description.abstractThis paper describes a region growing segmentation algorithm for medical ultrasound images. The algorithm starts with anisotropic diffusion filtering to reduce speckle noise without blurring the edges. Then, region growing is performed starting from a seed point, using a merging criterion that compares intensity gradients to the noise level inside the region. Finally, the boundaries are smoothed using morphological closing. The algorithm was evaluated with two simulated images and eleven phantom images and converged in 10 of them with accurate region delimitation. Preliminary results show that the proposed method can be used for ultrasound image segmentation and does not require previous knowledge of the anatomy of the structures. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.eng
dc.description.sponsorshipAlpinion Medical Systems;The Society of Photo-Optical Instrumentation Engineers (SPIE)
dc.format.mediumRecurso electrónico
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSPIE
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85020784325&doi=10.1117%2f12.2254518&partnerID=40&md5=7e28cc8593ae2e4f109d27c383ae818d
dc.sourceScopus2-s2.0-85020784325
dc.titleRegion growing segmentation of ultrasound images using gradients and local statistics
dcterms.bibliographicCitationKass, M., Witkin, A., Terzopoulos, D., Snakes: Active contour models (1988) International Journal of Computer Vision, 1 (4), pp. 321-331
dcterms.bibliographicCitationCaselles, V., Kimmel, R., Sapiro, G., Geodesic active contours (1997) International Journal of Computer Vision, 22 (1), pp. 61-79
dcterms.bibliographicCitationCootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., Active shape models-their training and application (1995) Computer Vision and Image Understanding, 61 (1), pp. 38-59
dcterms.bibliographicCitationZhan, Y., Shen, D., Deformable segmentation of 3-d ultrasound prostate images using statistical texture matching method (2006) Medical Imaging, IEEE Transactions on, 25 (3), pp. 256-272
dcterms.bibliographicCitationWang, W., Qin, J., Chui, Y.-P., Heng, P.-A., A multiresolution framework for ultrasound image segmentation by combinative active contours (2013) Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 1144-1147. , IEEE
dcterms.bibliographicCitationWang, W., Zhu, L., Qin, J., Chui, Y.-P., Li, B.N., Heng, P.-A., Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion (2014) Optics and Lasers in Engineering, 54, pp. 105-116
dcterms.bibliographicCitationGupta, D., Anand, R., Tyagi, B., A hybrid segmentation method based on Gaussian kernel fuzzy clustering and region based active contour model for ultrasound medical images (2015) Biomedical Signal Processing and Control, 16, pp. 98-112
dcterms.bibliographicCitationYu, Y., Cheng, J., Li, J., Chen, W., Chiu, B., Automatic prostate segmentation from transrectal ultrasound images (2014) Biomedical Circuits and Systems Conference (BioCAS), 2014 IEEE, pp. 117-120. , IEEE
dcterms.bibliographicCitationThakur, A., Anand, R.S., A local statistics based region growing segmentation method for ultrasound medical images (2004) International Journal of Signal Processing, 1 (2), pp. 141-146
dcterms.bibliographicCitationLee, L.-K., Liew, S.-C., Breast ultrasound automated roi segmentation with region growing (2015) Software Engineering and Computer Systems (ICSECS), 2015 4th International Conference on, pp. 177-182. , IEEE
dcterms.bibliographicCitationJensen, J.A., Field: A program for simulating ultrasound systems (1996) Medical and Biological Engineering and Computing, 34, pp. 351-352
dcterms.bibliographicCitationJensen, J.A., Svendsen, N.B., Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers (1992) IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 39 (2), pp. 262-267
dcterms.bibliographicCitationGiraldo-Guzman, J., Porto-Solano, O., Cadena-Bonfanti, A., Contreras-Ortiz, S.H., Speckle reduction in echocardiography by temporal compounding and anisotropic diffusion filtering (2015) Tenth International Symposium on Medical Information Processing and Analysis, 92871F-92871F, International Society for Optics and Photonics
datacite.rightshttp://purl.org/coar/access_right/c_16ec
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94f
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
dc.source.eventMedical Imaging 2017: Ultrasonic Imaging and Tomography
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1117/12.2254518
dc.subject.keywordsAnisotropic diffusion filtering
dc.subject.keywordsMedical ultrasound
dc.subject.keywordsRegion growing segmentation
dc.subject.keywordsAnisotropy
dc.subject.keywordsImaging systems
dc.subject.keywordsMedical imaging
dc.subject.keywordsOptical anisotropy
dc.subject.keywordsTomography
dc.subject.keywordsUltrasonic imaging
dc.subject.keywordsAnisotropic diffusion filtering
dc.subject.keywordsIntensity gradients
dc.subject.keywordsMedical ultrasound
dc.subject.keywordsMedical ultrasound images
dc.subject.keywordsMorphological closing
dc.subject.keywordsRegion growing
dc.subject.keywordsSegmentation algorithms
dc.subject.keywordsUltrasound image segmentation
dc.subject.keywordsImage segmentation
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.ccAtribución-NoComercial 4.0 Internacional
dc.identifier.instnameUniversidad Tecnológica de Bolívar
dc.identifier.reponameRepositorio UTB
dc.relation.conferencedate15 February 2017 through 16 February 2017
dc.type.spaConferencia
dc.identifier.orcid57190165939
dc.identifier.orcid57190688459
dc.identifier.orcid57210822856


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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.