dc.contributorDuric N.
dc.contributorHeyde 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.created2020-03-26T16:32:39Z
dc.date.issued2017
dc.identifierProgress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10139
dc.identifier9781510607231
dc.identifier16057422
dc.identifierhttps://hdl.handle.net/20.500.12585/8952
dc.identifier10.1117/12.2254518
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier57190165939
dc.identifier57190688459
dc.identifier57210822856
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.
dc.languageeng
dc.publisherSPIE
dc.relation15 February 2017 through 16 February 2017
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
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.sourceMedical Imaging 2017: Ultrasonic Imaging and Tomography
dc.titleRegion growing segmentation of ultrasound images using gradients and local statistics


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