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dc.contributor.authorScarparo, Daniele Cristina [UNESP]
dc.contributor.authorSalvadeo, Denis Henrique Pinheiro [UNESP]
dc.contributor.authorPedronette, Daniel Carlos Guimarães [UNESP]
dc.contributor.authorBarufaldi, Bruno
dc.contributor.authorMaidment, Andrew Douglas Arnold
dc.identifier.citationJournal of Medical Imaging, v. 6, n. 3, 2019.
dc.description.abstractDigital breast tomosynthesis (DBT) is an imaging technique created to visualize 3-D mammary structures for the purpose of diagnosing breast cancer. This imaging technique is based on the principle of computed tomography. Due to the use of a dangerous ionizing radiation, the as low as reasonably achievable (ALARA) principle should be respected, aiming at minimizing the radiation dose to obtain an adequate examination. Thus, a noise filtering method is a fundamental step to achieve the ALARA principle, as the noise level of the image increases as the radiation dose is reduced, making it difficult to analyze the image. In our work, a double denoising approach for DBT is proposed, filtering in both projection (prereconstruction) and image (postreconstruction) domains. First, in the prefiltering step, methods were used for filtering the Poisson noise. To reconstruct the DBT projections, we used the filtered backprojection algorithm. Then, in the postfiltering step, methods were used for filtering Gaussian noise. Experiments were performed on simulated data generated by open virtual clinical trials (OpenVCT) software and on a physical phantom, using several combinations of methods in each domain. Our results showed that double filtering (i.e., in both domains) is not superior to filtering in projection domain only. By investigating the possible reason to explain these results, it was found that the noise model in DBT image domain could be better modeled by a Burr distribution than a Gaussian distribution. Finally, this important contribution can open a research direction in the DBT denoising problem.en
dc.relation.ispartofJournal of Medical Imaging
dc.subjectBurr distribution
dc.subjectdigital breast tomosynthesis
dc.subjectdouble denoising
dc.subjectGaussian noise
dc.subjectnoise model
dc.subjectPoisson noise
dc.titleEvaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domainen
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionHospital of the University of Pennsylvania
dc.description.affiliationSão Paulo State University (Unesp) Institute of Geosciences and Exact Sciences
dc.description.affiliationUniversity of Pennsylvania Hospital of the University of Pennsylvania Department of Radiology
dc.description.affiliationUnespSão Paulo State University (Unesp) Institute of Geosciences and Exact Sciences
dc.rights.accessRightsAcesso aberto
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