CAD system for breast US images with speckle noise reduction and bio-inspired segmentation

dc.contributor.authorRODRIGUES, P. S. S.
dc.contributor.authorGuilherme Lopes
dc.contributor.authorGIRALDI, G. A.
dc.contributor.authorBARCELOS, C. A. Z.
dc.contributor.authorVIEIRA, L.
dc.contributor.authorGULIATO, D.
dc.contributor.authorKUMAR SINGH, B.
dc.contributor.authorOrcidhttps://orcid.org/0000-0003-0873-3236
dc.date.accessioned2022-01-12T21:56:08Z
dc.date.available2022-01-12T21:56:08Z
dc.date.issued2019-10-05
dc.description.abstract© 2019 IEEE.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%.
dc.description.firstpage68
dc.description.lastpage75
dc.identifier.citationRODRIGUES, P. S. S.; LOPES, G.; GIRALDI, G. A.; BARCELOS, C. A. Z.; VIEIRA, L.; GULIATO, D.; KUMAR SINGH, B.; KUMAR SINGH, B. CAD system for breast US images with speckle noise reduction and bio-inspired segmentation. Proceedings - 32nd Conference on Graphics, Patterns and Images, SIBGRAPI 2019, p. 68-75.
dc.identifier.doi10.1109/SIBGRAPI.2019.00018
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3704
dc.relation.ispartofProceedings - 32nd Conference on Graphics, Patterns and Images, SIBGRAPI 2019
dc.rightsAcesso Restrito
dc.subject.otherlanguageBio inspired Segmentation
dc.subject.otherlanguageBreast US Images
dc.subject.otherlanguageCAD System
dc.subject.otherlanguageSpeckle Noise Reduction
dc.titleCAD system for breast US images with speckle noise reduction and bio-inspired segmentation
dc.typeArtigo de evento
fei.scopus.citations2
fei.scopus.eid2-s2.0-85077078042
fei.scopus.subjectBreast US
fei.scopus.subjectCAD system
fei.scopus.subjectClassification results
fei.scopus.subjectComputer Aided Diagnosis(CAD)
fei.scopus.subjectHistogram equalizations
fei.scopus.subjectSpeckle noise reduction
fei.scopus.subjectStatistical moments
fei.scopus.subjectThe region of interest (ROI)
fei.scopus.updated2024-05-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077078042&origin=inward
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