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Previous issue date: 2016-02-22 / FACEPE / O c^ancer de mama e o mais comum entre as mulheres no mundo e no Brasil, depois do
de pele n~ao melanoma. De acordo com o Instituto Nacional de C^ancer, em 2013 foram
registradas 14.388 mortes devido a esta mol estia. O c^ancer de mama e uma preocupa c~ao n~ao
somente nacional, mas mundial. O m etodo utilizado para a sua detec c~ao e a mamogra a,
que e uma t ecnica de imagem que utiliza a emiss~ao Raios-X incidentes na mama e capta
a parte da radia c~ao n~ao absorvida pelos tecidos mam arios. A mamogra a e um exame
de dif cil an alise pelo motivo de, em muitos casos, a densidade tecidual do tumor ser
bastante parecida com a densidade de alguns tecidos saud aveis da mama. Uma abordagem
interessante e a utiliza c~ao de t ecnicas computadorizadas de aux lio ao diagn ostico, ou
seja, ferramentas baseadas em processamento de imagens e intelig^encia computacional
projetadas para o apoio ao pro ssional radiologista. Estudos pr evios demonstram que
considerar a domin^ancia tecidual mam aria nas ferramentas computacionais de apoio ao
diagn ostico melhora consideravelmente as taxas de acerto. Para este trabalho, e proposta
a constru c~ao de um sistema de classi ca c~ao de tumores de mama baseado descritores de
Zernike como um descritor de forma das les~oes de mama, associado as m aquinas de vetor
de suporte como classi cador. S~ao comparadas diferentes t ecnicas de sele c~ao de atributos
com o objetivo de reduzir o custo computacional do sistema, mas sempre levando em conta
a necessidade de se manter altas taxas de acerto, j a que isto pode re
etir em erros de
diagn ostico de c^ancer de mama. Atrav es dos dados analisados, e notado que a t ecnica linear
de an alise de componentes principais (aliada a transformada de wavelets morfol ogica como
etapa de pr e-processamento) se mostrou uma otima t ecnica para realiza c~ao de redu c~ao de
atributos com um menor impacto nas taxas de acerto do sistema de apoio ao diagn ostico
do c^ancer de mama, onde s~ao obtidas taxas de m edias de redu c~ao de acerto em torno de 2%
(uma queda m edia de aproximadamente 95% para 93%), onde a redu c~ao do tamanho do
vetor de atributos e de cerca de 64% (dentre os diferentes tipos de tecido, s~ao selecionados
de 70 a 89 atributos do total de 224). / Breast cancer is one of the most common type of cancer among women. According to
Brazil's national institute of cancer, in 2013 it was registered 14,388 deaths due to this
disease. Breast cancer is not only a national but worldwide concern. The most used method
to its detection is mammography which is an image technique that uses X ray emission
and measures the non-absorbed radiation by the breast internal tissues. Mammography
is a hard to analyze image exam, mainly because in many cases tumor's density is much
alike some of the healthy tissues' density. An interesting approach is the use of computeraided
techniques for diagnosis, meaning the use of image processing and computational
intelligence tools designed to support and aid radiologists in their tasks. Previous studies
show that considering the di erent types of breast tissue dominance improves considerably
the rate of correct classi cation by these computational tools. It is proposed for this work
the development of a breast tumor classi cation system based on Zernike descriptors as
shape descriptors of these breast lesions along with support vector machines as machine
learning algorithms for classi cation. Some feature selection techniques are compared for
reducing the whole system computational cost but always taking into consideration that
the classi cation rates must be kept as high as possible. Of the techniques studied in this
work, principal components analysis along with morphological wavelet transform for image
preprocessing has shown itself as a great technique for feature reduction with lesser impact
on classi cation rates. It was achieved a mean 2% loss in those rates (from about 95% to
93% as mean values) with a mean feature reduction of about 64% (in the range of 70 to
89 features from 224).
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufpe.br:123456789/17901 |
| Date | 22 February 2016 |
| Creators | ROCHA, Arthur Diego Dias |
| Contributors | http://lattes.cnpq.br/5161747893799561, SANTOS, Wellington Pinheiro dos, SOUZA, Ricardo Emmanuel de |
| Publisher | Universidade Federal de Pernambuco, Programa de Pos Graduacao em Engenharia Biomedica, UFPE, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Breton |
| Detected Language | Portuguese |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional da UFPE, instname:Universidade Federal de Pernambuco, instacron:UFPE |
| Rights | Attribution-NonCommercial-NoDerivs 3.0 Brazil, http://creativecommons.org/licenses/by-nc-nd/3.0/br/, info:eu-repo/semantics/openAccess |
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