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dc.contributor.authorunicampFalcão, Alexandre Xavierpt_BR
dc.titleDetection of tooth fractures in CBCT images using attention index estimationpt_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, A., Carestream Health, Inc., Research and Innovation Labs, 1049 Ridge Road West, Rochester, NY, United Statespt, 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.publisher.cityBellingham, WApt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInternational Society for Optical Engineeringpt_BR
dc.identifier.citationProgress In Biomedical Optics And Imaging - Proceedings Of Spie. Spie, v. 9036, n. , p. - , 2014.pt_BR
dc.description.sponsordocumentnumber303673/2010-9; 479070/2013-0pt_BR
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
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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.type.formArtigo de eventopt_BR
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