Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/320563
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbnpt_BR
dc.contributor.authorunicampRocha, Anderson de Rezendept_BR
dc.contributor.authorunicampMenezes, Fábio Hüsemannpt_BR
dc.contributor.authorunicampTorres, Ricardo da Silvapt_BR
dc.contributor.authorunicampMendes Júnior, Pedro Ribeiropt_BR
dc.contributor.authorunicampRafael de Oliveira Werneckpt_BR
dc.contributor.authorunicampAlmeida, Waldir Rodrigues dept_BR
dc.contributor.authorunicampStein, Bernardo Vecchiapt_BR
dc.contributor.authorunicampPazinato, Daniel Vatanabept_BR
dc.typeArtigopt_BR
dc.titlePixel-level tissue classification for ultrasound imagespt_BR
dc.contributor.authorPazinato, Daniel V.pt_BR
dc.contributor.authorStein, Bernardo V.pt_BR
dc.contributor.authorAlmeida, Waldir R. dept_BR
dc.contributor.authorWerneck, Rafael de O.pt_BR
dc.contributor.authorMendes Júnior, Pedro R.pt_BR
dc.contributor.authorPenatti, Otávio A. B.pt_BR
dc.contributor.authorTorres, Ricardo da S.pt_BR
dc.contributor.authorMenezes, Fábio H.pt_BR
dc.contributor.authorRocha, Andersonpt_BR
unicamp.author.emaildaniel.pazinato@students.ic.unicamp.br; bernardo.stein@students.ic.unicamp.br; waldir.almeida@students.ic.unicamp.br; rafael.werneck@ic.unicamp.br; pedro.junior@ic.unicamp.br; o.penatti@samsung.com; rtorres@ic.unicamp.br; fmenezes@fcm.unicamp.br; anderson.rocha@ic.unicamp.brpt_BR
dc.subjectProcessamento de imagem assistida por computadorpt_BR
dc.subjectSegmentação de imagenspt_BR
dc.subjectAnálise de imagempt_BR
dc.subjectMultiescalapt_BR
dc.subjectUltra-sompt_BR
dc.subject.otherlanguageImage processing computer-assistedpt_BR
dc.subject.otherlanguageImage segmentationpt_BR
dc.subject.otherlanguageImage analysispt_BR
dc.subject.otherlanguageMultiscalept_BR
dc.subject.otherlanguageUltrasoundpt_BR
dc.description.abstractPixel-level tissue classification for ultrasound images, commonly applied to carotid images, is usually based on defining thresholds for the isolated pixel values. Ranges of pixel values are defined for the classification of each tissue. The classification of pixels is then used to determine the carotid plaque composition and, consequently, to determine the risk of diseases (e.g., strokes) and whether or not a surgery is necessary. The use of threshold-based methods dates from the early 2000s but it is still widely used for virtual histology. Methodology/Principal Findings: We propose the use of descriptors that take into account information about a neighborhood of a pixel when classifying it. We evaluated experimentally different descriptors (statistical moments, texture-based, gradient-based, local binary patterns, etc.) on a dataset of five types of tissues: blood, lipids, muscle, fibrous, and calcium. The pipeline of the proposed classification method is based on image normalization, multiscale feature extraction, including the proposal of a new descriptor, and machine learning classification. We have also analyzed the correlation between the proposed pixel classification method in the ultrasound images and the real histology with the aid of medical specialists. Conclusions/Significance: The classification accuracy obtained by the proposed method with the novel descriptor in the ultrasound tissue images (around 73%) is significantly above the accuracy of the state-of-the-art threshold-based methods (around 54%). The results are validated by statistical tests. The correlation between the virtual and real histology confirms the quality of the proposed approach showing it is a robust ally for the virtual histology in ultrasound images.en
dc.description.abstractPixel-level tissue classification for ultrasound images, commonly applied to carotid images, is usually based on defining thresholds for the isolated pixel values. Ranges of pixel values are defined for the classification of each tissue. The classificatiopt_BR
dc.relation.ispartofIEEE journal of biomedical and health informaticspt_BR
dc.relation.ispartofabbreviationIEEE j. biomed. health inform.pt_BR
dc.publisher.cityPiscataway, NJpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInstitute of Electrical and Electronics Engineerspt_BR
dc.date.issued2016pt_BR
dc.date.monthofcirculationJan.pt_BR
dc.identifier.citationIeee Journal Of Biomedical And Health Informatics. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, n. 20, n. 1, p. 256 - 267.pt_BR
dc.language.isoengpt_BR
dc.description.volume20pt_BR
dc.description.issuenumber1pt_BR
dc.description.issuesupplementpt_BR
dc.description.issuepartpt_BR
dc.description.issuespecialpt_BR
dc.description.firstpage256pt_BR
dc.description.lastpage267pt_BR
dc.rightsfechadopt_BR
dc.rightsFechadopt_br
dc.sourceWOSpt_BR
dc.identifier.issn2168-2194pt_BR
dc.identifier.eissn2168-2208pt_BR
dc.identifier.wosidWOS:000372629700030pt_BR
dc.identifier.doi10.1109/JBHI.2014.2386796pt_BR
dc.identifier.urlhttps://ieeexplore.ieee.org/document/7000013pt_BR
dc.description.sponsorshipCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOpt_BR
dc.description.sponsorshipCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORpt_BR
dc.description.sponsorshipFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOpt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorship1Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)pt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.description.sponsordocumentnumber2012/20465-5pt_BR
dc.description.sponsordocumentnumber2012/20465-5pt_BR
dc.description.sponsordocumentnumber2012/20465-5pt_BR
dc.date.available2016-12-06T18:32:31Z-
dc.date.accessioned2016-12-06T18:32:31Z-
dc.description.provenanceMade available in DSpace on 2016-12-06T18:32:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2016. Added 1 bitstream(s) on 2021-01-04T14:27:15Z : No. of bitstreams: 1 000372629700030.pdf: 787570 bytes, checksum: 4ee1d768bf5939de755c1c6c9bcd425a (MD5)en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/320563-
dc.description.conferencenomept_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.departmentDepartamento de Cirurgiapt_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeFaculdade de Ciências Médicaspt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordCarotid plaque compositionpt_BR
dc.subject.keywordPixel-level tissue classificationpt_BR
dc.subject.keywordUltrasound imagespt_BR
dc.subject.keywordVirtual histology (VH)pt_BR
dc.identifier.source000372629700030pt_BR
dc.creator.orcid0000-0002-4236-8216pt_BR
dc.creator.orcidsem informaçãopt_BR
dc.creator.orcid0000-0001-9772-263Xpt_BR
dc.creator.orcid0000-0001-8086-018Xpt_BR
dc.creator.orcid0000-0002-8217-7250pt_BR
dc.creator.orcid0000-0002-5848-5560pt_BR
dc.creator.orcid0000-0001-8620-9785pt_BR
dc.creator.orcidsem informaçãopt_BR
dc.type.formArtigopt_BR
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