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

Authors: Roberto Pavusa Junior; João C.L. Fernandes; Alessandro P. Da Silva; Marcia A.S. Bissaco; Silvia R.M.S. Boschi; Terigi A. Scardovelli; Silvia C. Martini

Addresses: Department of Biomedical Engineering, University of Mogi das Cruzes, Mogi das Cruzes, São Paulo, Brazil ' Department of Biomedical Engineering, University of Mogi das Cruzes, Mogi das Cruzes, São Paulo, Brazil; Department of Development and Research, ENIAC University Center, Guarulhos, São Paulo, Brazil ' Department of Biomedical Engineering, University of Mogi das Cruzes, Mogi das Cruzes, São Paulo, Brazil ' Department of Biomedical Engineering, University of Mogi das Cruzes, Mogi das Cruzes, São Paulo, Brazil ' Department of Biomedical Engineering, University of Mogi das Cruzes, Mogi das Cruzes, São Paulo, Brazil ' Department of Biomedical Engineering, University of Mogi das Cruzes, Mogi das Cruzes, São Paulo, Brazil ' Department of Biomedical Engineering, University of Mogi das Cruzes, Mogi das Cruzes, São Paulo, Brazil

Abstract: 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.

Keywords: breast density; mammography; computer-aided diagnosis; support vector machine; SVM; k nearest neighbour; kNN; speeded up robust feature; SURF.

DOI: 10.1504/IJBET.2022.124185

International Journal of Biomedical Engineering and Technology, 2022 Vol.39 No.3, pp.213 - 248

Received: 23 Oct 2018
Accepted: 26 Apr 2019
Published online: 18 Jul 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article