Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorNascimento, Marcelo Zanchetta Do-
Autor(es): dc.creatorMartins, Alessandro Santana-
Autor(es): dc.creatorNeves, Leandro Alves-
Autor(es): dc.creatorRamos, Rodrigo Pereira-
Autor(es): dc.creatorFlores, Edna Lúcia-
Autor(es): dc.creatorCarrijo, Gilberto Arantes-
Data de aceite: dc.date.accessioned2021-03-10T19:56:54Z-
Data de disponibilização: dc.date.available2021-03-10T19:56:54Z-
Data de envio: dc.date.issued2014-05-27-
Data de envio: dc.date.issued2014-05-27-
Data de envio: dc.date.issued2013-06-21-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2013.04.036-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/75693-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/75693-
Descrição: dc.descriptionBreast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.-
Formato: dc.format6213-6221-
Idioma: dc.languageen-
Relação: dc.relationExpert Systems with Applications-
Relação: dc.relation3.768-
Relação: dc.relation1,271-
Direitos: dc.rightsclosedAccess-
Palavras-chave: dc.subjectMammography-
Palavras-chave: dc.subjectPolynomial classifier-
Palavras-chave: dc.subjectTexture analysis-
Palavras-chave: dc.subjectWavelet CAD-
Palavras-chave: dc.subjectArea under the ROC curve-
Palavras-chave: dc.subjectArtificial intelligence algorithms-
Palavras-chave: dc.subjectClassification algorithm-
Palavras-chave: dc.subjectDigitized mammograms-
Palavras-chave: dc.subjectReceiver operating characteristics curves (ROC)-
Palavras-chave: dc.subjectWavelet domain features-
Palavras-chave: dc.subjectAlgorithms-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectComputer aided diagnosis-
Palavras-chave: dc.subjectDecision trees-
Palavras-chave: dc.subjectDiscrete wavelet transforms-
Palavras-chave: dc.subjectDiseases-
Palavras-chave: dc.subjectMultiresolution analysis-
Palavras-chave: dc.subjectOrthogonal functions-
Palavras-chave: dc.subjectTextures-
Palavras-chave: dc.subjectX ray screens-
Palavras-chave: dc.subjectPolynomials-
Título: dc.titleClassification of masses in mammographic image using wavelet domain features and polynomial classifier-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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