DETECÇÃO DE REGIÕES SUSPEITAS E CLASSIFICAÇÃO DE MASSAS EM MAMOGRAFIAS DIGITAIS UTILIZANDO DESCRIÇÃO ESPACIAL COM FUNÇÃO VARIOGRAMA

• Ano de defesa: 2011
• Autor(a) principal: Ericeira, Daniel Rodrigues
• Orientador(a): SILVA, Aristófanes Corrêa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: Câncer de Mama; Mamografia; Variograma; Variograma Cruzado; Máquina de Vetores de Suporte
• Palavras-chave em Inglês: Breast Cancer; Mammography; Variogram; Cross Variogram; Support Vector Machine
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/461

Mammography is the exam of the breast, used as breast cancer prevention and also as a diagnostic method. This exam, which consists in an X-Ray of the breast, allows cancer detection. The purpose of this work is to use image processing techniques and computer vision to help specialists in detecting suspect regions and masses in digital mammographies. The first stage of the methodology consists in pre-processing the images to make them more suitable to registration, through noise reduction, image segmentation and re-scale. The next stage presents bilateral left and right breast image pairs registration. In order to correct position and compression differences that occur during the exams, rigid registration (followed by optic flow deformable registration) was applied in each image pair. Corresponding pairs of regions were related and their mutual variations were measured through cross-variogram spatial description. On the next stage, a training model for a Support Vector Machine (SVM) was created using as characteristics the cross-variogram values of each pair of regions of 180 cases. This SVM was tested for 100 new cases. The region pairs that contained lesions were classified as suspect regions , and the other regions as non-suspect regions . From the suspect regions, variogram characteristics were extracted as tissue texture descriptors. The regions that contained masses were classified as mass regions and the other regions as non-mass regions . Stepwise linear discriminant analysis was applied to select the most significant characteristics to train the second SVM. Tests with 30 new cases were performed for the trained SVM final classification in mass or non-mass . The best case presented on the final classification: 96% accuracy, 100% sensitivity and 95,34% specificity. The worst case presented: 70% accuracy, 100% sensitivity and 67,56% specificity. On average, the 30 cases presented: 90% accuracy, 100% sensitivity and 85% specificity.