Lymphoma images analysis using morphological and non-morphological descriptors for classification

dc.contributor.authordo Nascimento, Marcelo Zanchetta
dc.contributor.authorMartins, Alessandro Santana
dc.contributor.authorAzevedo Tosta, Thaína Aparecida
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.institutionUFU - FACOM
dc.contributor.institutionIFTM
dc.contributor.institutionUniversidade Federal do ABC (UFABC)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:20:40Z
dc.date.available2018-12-11T17:20:40Z
dc.date.issued2018-09-01
dc.description.abstractMantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.en
dc.description.affiliationUFU - FACOM, av. João Neves de Ávila 2121, Bl.B
dc.description.affiliationIFTM, r. Belarmino Vilela Junqueira S/N
dc.description.affiliationUFABC - CMCC, av. dos Estados 5001, Bl.B
dc.description.affiliationUNESP - DCCE, r. Cristóvão Colombo 2265
dc.description.affiliationUnespUNESP - DCCE, r. Cristóvão Colombo 2265
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.description.sponsorshipIdCNPq: 427114/2016-0
dc.description.sponsorshipIdFAPEMIG: TEC-APQ-02885-15
dc.format.extent65-77
dc.identifierhttp://dx.doi.org/10.1016/j.cmpb.2018.05.035
dc.identifier.citationComputer Methods and Programs in Biomedicine, v. 163, p. 65-77.
dc.identifier.doi10.1016/j.cmpb.2018.05.035
dc.identifier.file2-s2.0-85048075793.pdf
dc.identifier.issn1872-7565
dc.identifier.issn0169-2607
dc.identifier.scopus2-s2.0-85048075793
dc.identifier.urihttp://hdl.handle.net/11449/176402
dc.language.isoeng
dc.relation.ispartofComputer Methods and Programs in Biomedicine
dc.relation.ispartofsjr0,786
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectHistological image
dc.subjectLymphoma
dc.subjectMorphological and non-morphological features
dc.subjectPolynomial
dc.subjectSVM
dc.titleLymphoma images analysis using morphological and non-morphological descriptors for classificationen
dc.typeArtigo
unesp.author.orcid0000-0003-3537-0178 0000-0003-3537-0178[1]

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