Automated methodology for breast segmentation and mammographic density classification using co-occurrence and statistical and SURF descriptors

Autor(es)

Pavusa Junior, Roberto ; Fernandes, Joao C. L. ; da Silva, Alessandro P. ; Bissaco, Marcia A. S. ; Boschi, Silvia R. M. S. ; Scardovelli, Terigi A. ; Martini, Silvia C.

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY; v. 39, n. 3, p. 36-pg., 2022-01-01.

Resumo

This paper presents a fully automated methodology for segmenting and classifying mammographic images. We developed a new set of descriptors for determination of breast density based in the standard MIAS database. We performed the image pre-processing, image laterality detection and background and artefacts removal as well as pectoral muscle identification and segmentation. From the segments named breast and pectoral muscle, descriptors from histogram, co-occurrence matrix, and points of interest analysis, were extracted. The descriptors were Spearman correlation analysis, principal component analysis and linear discriminant analysis. The image classification was performed through the classifiers k nearest neighbours (kNNs) and support vector machine (SVM). An accuracy of 72.05% was achieved with the SVM classifier, and of 91.30% with the kNN. The proposed methodology is promising, as well as the descriptors used for the breast density classification, which surpassed most of the studies found in the literature that used all images from the MIAS database. (AU)

• Processo FAPESP: 17/14016-7 - Ambiente web para aprendizado e treinamento de graduandos, residentes ou médicos na interpretação de imagens de mama
• Beneficiário: Silvia Cristina Martini Rodrigues
• Modalidade de apoio: Auxílio à Pesquisa - Regular