%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2019/09.09.23.44
%2 sid.inpe.br/sibgrapi/2019/09.09.23.44.19
%@doi 10.1109/SIBGRAPI.2019.00018
%T CAD System for Breast US Images with Speckle Noise Reduction and Bio-inspired Segmentation
%D 2019
%A Rodrigues, Paulo Sergio Silva,
%A Lopes, Guilherme A. Wachs,
%A Giraldi, Gilson A.,
%A Barcelos, Celia A. Z.,
%A Vieira, Luciana,
%A Guliato, Denise,
%A Singh, Bikesh Kumar,
%@affiliation Computer Science Department, Centro Universitário FEI
%@affiliation Computer Science Department, Centro Universitário FEI
%@affiliation National Laboratory for Scientific Computing
%@affiliation Federal University of Uberlandia
%@affiliation Federal University of Uberlandia
%@affiliation Federal University of Uberlandia
%@affiliation Department of Biomedical Engineering, National Institute of Technology Raipur
%E Oliveira, Luciano Rebouças de,
%E Sarder, Pinaki,
%E Lage, Marcos,
%E Sadlo, Filip,
%B Conference on Graphics, Patterns and Images, 32 (SIBGRAPI)
%C Rio de Janeiro, RJ, Brazil
%8 28-31 Oct. 2019
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K CAD System, Ultrasound images, Speckle Noise Reduction, Bio-inspired Segmentation.
%X Ultrasound (US) images are highly susceptible to speckle-like noise which makes imperative to use specific techniques for image smoothing. However, this process can lead to undesirable side effects such as the degradation of the real contour of the region of interest (ROI). In such context, this paper presents a new methodology for computer aided diagnosis (CAD) systems whose heart is the combination of a method for speckle noise reduction, with histogram equalization and a technique for image segmentation that uses the bio-inspired firefly algorithm and Bayesian model. The segmentation approach and the equalization are applied in two distinct stages: globally and locally. The global application produces an initial coarse estimate of the ROI, and the local application defines this region more precisely. In the classification step we carried out experiments which show that the combination of features computed both within and below the lesion strongly influences the final accuracy. We show that the gray-scale distribution and statistical moments within the lesion together with gray-scale distribution and contrast of the region below the lesion is the combination that produces the better classification results. Experiments in a database of 250 US images of breast anomalies (100 benign and 150 malignant) show that the proposed methodology reaches performance of 95%.
%@language en
%3 PID6125907.pdf