Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/86483
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn9780819498298pt
dc.identifier.isbn978-0-81949-829-8pt_BR
dc.contributor.authorunicampFalcão, Alexandre Xavierpt_BR
dc.typeArtigopt_BR
dc.titleDetection of tooth fractures in CBCT images using attention index estimationpt_BR
dc.title.alternativept_BR
dc.contributor.authorSouza, Andrept_BR
dc.contributor.authorFalcão, Alexandrept_BR
dc.contributor.authorRay, Lawrencept_BR
unicamp.authorFalcão, A., University of Campinas (UNICAMP), Institute of Computing, Av. Albert Einstein, 1251, Campinas, SP, Brazilpt_BR
unicamp.author.externalSouza, A., Carestream Health, Inc., Research and Innovation Labs, 1049 Ridge Road West, Rochester, NY, United Statespt
unicamp.author.externalRay, L., Carestream Health, Inc., Research and Innovation Labs, 1049 Ridge Road West, Rochester, NY, United Statespt
dc.subjectAprendizado de máquinapt_BR
dc.subjectReconhecimento de padrõespt_BR
dc.subjectProcessamento de imagenspt_BR
dc.subjectInteligência artificialpt_BR
dc.subjectSegmentação de imagenspt_BR
dc.subject.otherlanguageMachine learningpt_BR
dc.subject.otherlanguagePattern recognitionpt_BR
dc.subject.otherlanguageImage processingpt_BR
dc.subject.otherlanguageArtificial intelligencept_BR
dc.subject.otherlanguageImage segmentationpt_BR
dc.description.abstractThe attention index (φ) is a number from zero to one that indicates a possible fracture is detected inside a selected tooth. The higher the φ number, the greater the likelihood for needed attention in the visual examination. The method developed for the φ estimation extracts a connected component with image properties that are similar to those of a typical tooth fracture. That is, in cone-beam computed tomography (CBCT) images, a fracture appears as a dark canyon inside the tooth. In order to start the visual examination process, the method provides a plane across the geometric center of the suspicious fracture component, which maximizes the number of pixels from that component inside the plane. During visual examination, the user (doctor) can change plane orientations and locations, by manipulating the mouse toward different graphical elements that represent the plane on a 3-D rendition of the tooth, while the corresponding image of the plane is shown at its side. The visual examination aims at confirming or disproving the fracture-detection event. We have designed and implemented these algorithms using the image-foresting transform methodology. © 2014 SPIE.en
dc.description.abstractThe attention index (φ) is a number from zero to one that indicates a possible fracture is detected inside a selected tooth. The higher the φ number, the greater the likelihood for needed attention in the visual examination. The method developed for thept_BR
dc.relation.ispartofProgress in biomedical optics and imagingpt_BR
dc.relation.ispartofabbreviationpt_BR
dc.publisher.cityBellingham, WApt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInternational Society for Optical Engineeringpt_BR
dc.date.issued2014pt_BR
dc.date.monthofcirculationpt_BR
dc.identifier.citationProgress In Biomedical Optics And Imaging - Proceedings Of Spie. Spie, v. 9036, n. , p. - , 2014.pt_BR
dc.language.isoengpt_BR
dc.description.volume9036pt_BR
dc.description.issuesupplementpt_BR
dc.description.issuepartpt_BR
dc.description.issuespecialpt_BR
dc.description.lastpagept_BR
dc.rightsfechadopt_BR
dc.rightsFechadopt_br
dc.sourceSCOPUSpt_BR
dc.identifier.issn1605-7422pt_BR
dc.identifier.eissnpt_BR
dc.identifier.doi10.1117/12.2041708pt_BR
dc.identifier.urlhttps://spie.org/Publications/Proceedings/Paper/10.1117/12.2041708pt_BR
dc.description.sponsorshipCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOpt_BR
dc.description.sponsordocumentnumber303673/2010-9; 479070/2013-0pt_BR
dc.date.available2015-06-25T17:53:33Z
dc.date.available2015-11-26T14:23:26Z-
dc.date.accessioned2015-06-25T17:53:33Z
dc.date.accessioned2015-11-26T14:23:26Z-
dc.description.provenanceMade available in DSpace on 2015-06-25T17:53:33Z (GMT). No. of bitstreams: 1 2-s2.0-84902160299.pdf: 845177 bytes, checksum: 255ed103b741e3760e378f1595d41767 (MD5) Previous issue date: 2014 Bitstreams deleted on 2021-01-04T14:25:58Z: 2-s2.0-84902160299.pdf,. Added 1 bitstream(s) on 2021-01-04T14:26:56Z : No. of bitstreams: 2 2-s2.0-84902160299.pdf: 950454 bytes, checksum: 2c301e3bfc1e7c01b2f357c94ea404b7 (MD5) 2-s2.0-84902160299.pdf.txt: 31166 bytes, checksum: 5ddc5ab766b5f912d2c6273379c125c6 (MD5)en
dc.description.provenanceMade available in DSpace on 2015-11-26T14:23:26Z (GMT). No. of bitstreams: 2 2-s2.0-84902160299.pdf: 845177 bytes, checksum: 255ed103b741e3760e378f1595d41767 (MD5) 2-s2.0-84902160299.pdf.txt: 31166 bytes, checksum: 5ddc5ab766b5f912d2c6273379c125c6 (MD5) Previous issue date: 2014en
dc.identifier.urihttp://www.repositorio.unicamp.br/handle/REPOSIP/86483
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/86483-
dc.identifier.idScopus2-s2.0-84902160299pt_BR
dc.description.referenceFeldkamp, L.A., Davis, L.C., Kress, J.W., Practical cone-beam algorithm (1984) J. Opt. Soc. Am. A, 1, pp. 612-619pt_BR
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dc.description.referencePatel, S., Dawood, A., Ford, T.P., Whaites, E., The potential applications of cone beam computed tomography in the management of endodontic problems (2007) Int. Endod. J., 40 (10), pp. 818-830pt_BR
dc.description.referenceHassan, B., Metska, M.E., Ozok, A.R., Van Der-Stelt, P., Wesselink, P.R., Detection of vertical fractures in endodontically treated teeth by a cone beam computed tomography scan (2009) J. Endod., 35 (5), pp. 719-722pt_BR
dc.description.referencePalomo, L., Palomo, J., Cone beam CT for diagnosis and planning in trauma cases (2009) Dent. Clin. North Am., 53 (4), pp. 717-727pt_BR
dc.description.referenceSouza, A., Falcão, A., Ray, L., 3-D examination of dental fractures with minimum user intervention (2013) Proc SPIE, 8671, pp. 86712Kpt_BR
dc.description.referenceFalcão, A., Stolfi, J., Lotufo, R., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. Pattern Anal. Mach. Intell., 26 (1), pp. 19-29pt_BR
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dc.description.referenceChiachia, G., Falcão, A., Rocha, A., Multiscale parameter search (MSPS): A deterministic approach for blackbox global optimization (2011) Tech. Report IC 11-15, , Institute of Computing - Unicamp, Campinas-SP, Brazilpt_BR
dc.description.referenceBergo, F., Falcão, A., Miranda, P., Rocha, L., Automatic image segmentation by tree pruning (2007) J. Math. Imaging Vis., 29, pp. 141-162pt_BR
dc.description.referenceMiranda, P., Falcão, A., Links between image segmentation based on optimum-path forest and minimum cut in graph (2009) J. Math. Imaging Vis., 35, pp. 128-142pt_BR
dc.description.referenceCiesielski, K., Udupa, J., Falcão, A., Miranda, P., Fuzzy connectedness image segmentation in graph cut formulation: A linear-time algorithm and a comparative analysis (2012) J. Math. Imaging Vis., 44, pp. 375-398pt_BR
dc.description.referenceSchroeder, W., Martin, K., Lorensen, B., (2006) The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, , 4th Edition, Kitware, Clifton Park, NYpt_BR
dc.description.referenceRashedi, E., Nezamabadi-Pour, H., Saryazdi, S., GSA: A gravitational search algorithm (2009) Inf. Sci., 179 (13), pp. 2232-2248pt_BR
dc.description.referenceFalcão, A., Cunha, B.S., Lotufo, R.A., Design of connected operators using the image foresting transform (2001) Proc SPIE, 4322, pp. 468-479pt_BR
dc.description.referenceFalcão, A., Costa, L.F., Cunha, B.S., Multiscale skeletons by image foresting transform and its applications to neuromorphometry (2002) Pattern Recognition, 35 (7), pp. 1569-1580pt_BR
dc.description.conferencenomeSPIE - international society for optical engineering. medical imagingpt_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordMachine learningpt_BR
dc.subject.keywordPattern detectionpt_BR
dc.subject.keywordImage-based modelpt_BR
dc.subject.keyword3-D image segmentationpt_BR
dc.identifier.source2-s2.0-84902160299pt_BR
dc.creator.orcid0000-0002-2914-5380pt_BR
dc.type.formArtigo de eventopt_BR
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