Detalhes bibliográficos
| Ano de defesa: |
2012 |
| Autor(a) principal: |
COSTA, Daniel Duarte
 |
| Orientador(a): |
BARROS FILHO, Allan Kardec Duailibe
|
| Banca de defesa: |
NASCIMENTO, Maria do Desterro Soares Brandão
,
CHEIN, Maria Bethânia da Costa |
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Maranhão
|
| Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUAÇÃO EM BIOTECNOLOGIA - RENORBIO/CCBS
|
| Departamento: |
Fertilização
|
| País: |
BR
|
| Palavras-chave em Português: |
|
| Palavras-chave em Inglês: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://tedebc.ufma.br:8080/jspui/handle/tede/64
|
Resumo: |
Breast cancer is the leading cause of cancer death among women in Western countries. To improve the accuracy of diagnosis by radiologists and doing it so early, new computer vision systems have been developed and improved with the passage of time. Some methods of the detection and classification of lesions in mammography images for computer systems diagnostic (CAD) were developed using different statistical techniques. In this thesis, we present methodologies of CADs systems to detect and classify mass regions in mammographic images, from two image databases: DDSM and MIAS. The results show that it is possible by these methods to obtain a detection rate of up to 96% of mass regions, using efficient coding technique and K-means clustering algorithm. To classify regions in mass or non-mass correctly, was obtained a success rate up to 90% using the independent component analysis (ICA) and linear discriminant analysis (LDA). From these results generated a web application, called SADIM (Sistema de Auxílio a Diagnóstico de Imagem Mamográfica), which can be used by any registered professional. |
