dc.creator | Guillen J.E.I. | |
dc.creator | Contreras Ortiz, Sonia Helena | |
dc.date.accessioned | 2020-03-26T16:33:03Z | |
dc.date.available | 2020-03-26T16:33:03Z | |
dc.date.created | 2020-03-26T16:33:03Z | |
dc.date.issued | 2019 | |
dc.identifier | 2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings | |
dc.identifier | 9781728114910 | |
dc.identifier | https://hdl.handle.net/20.500.12585/9148 | |
dc.identifier | 10.1109/STSIVA.2019.8730287 | |
dc.identifier | Universidad Tecnológica de Bolívar | |
dc.identifier | Repositorio UTB | |
dc.identifier | 57209541901 | |
dc.identifier | 57210822856 | |
dc.description.abstract | Ultrasound imaging is a safe and cost-effective diagnostic tool, but the quality of the images is affected by speckle noise and artifacts. Anisotropic diffusion filters can be used to reduce noise and preserve the edges in the image. However, this technique is very sensitive to the number of iterations selected. This paper proposes a stopping criterion for effective noise removal without blurring the edges, based on the relative variance between the estimated denoised image and the original one. Different quality metrics were evaluated in 25 test images. The results suggest that the proposed stopping criterion can be implemented efficiently and aids in the process of automation of the filter. © 2019 IEEE. | |
dc.language | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.relation | 24 April 2019 through 26 April 2019 | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.rights | Atribución-NoComercial 4.0 Internacional | |
dc.source | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068081454&doi=10.1109%2fSTSIVA.2019.8730287&partnerID=40&md5=547d371e28c0c01ef46a5d37bd2fb3a8 | |
dc.source | Scopus2-s2.0-85068081454 | |
dc.source | 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 | |
dc.title | Selection of a Stopping Criterion for Anisotropic Diffusion Filtering in Ultrasound Images | |