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dc.contributor.authorLiasis, G.en
dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorPetroudi, Stylianien
dc.creatorLiasis, G.en
dc.creatorPattichis, Constantinos S.en
dc.creatorPetroudi, Stylianien
dc.description.abstractMammographic breast density refers to the prevalence of fibroglandular tissue as it appears on a mammogram. Breast density is not only an important risk for developing breast cancer but can also mask abnormalities. Breast density information can be used for planning individualized screening and treatment. In this work, statistical distributions of different texture descriptors and their combination are investigated with Support Vector Machines (SVMs) for objective breast density classification: Scale Invariant Feature Transforms (SIFT), Local Binary Patterns (LBP) and texton histograms. SIFT is an approach for detecting and extracting local feature descriptors that are reasonably invariant to changes in illumination, image noise, rotation, scaling and small changes in viewpoint. The SIFT descriptor is a coarse descriptor of the edges found in the keypoints. LBPs provide a robust and computationally simple way for describing pure local binary patterns in a texture. They provide information regarding the prevalence of different edge patterns and uniformity. Textons are defined under the operational definition of clustered filter responses and provide a statistical and structural unifying approach for texture characterization. The breast density classification accuracy of the SVM classifiers modeled on the histograms of the three different sets of texture features separately and their combination is evaluated on the Medical Image Analysis Society (MIAS) mammographic database and the results are presented. The combination of the statistical distributions of all the different texture features allows for the highest classification accuracy, reaching over 93%. © 2012 IEEE.en
dc.sourceIEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012en
dc.source12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012en
dc.subjectOperational definitionen
dc.subjectStatistical distributionen
dc.subjectClassification (of information)en
dc.subjectGraphic methodsen
dc.subjectSupport vector machinesen
dc.subjectImage segmentationen
dc.subjectTexture featuresen
dc.subjectSVM classifiersen
dc.subjectMedical image analysisen
dc.subjectBreast Canceren
dc.subjectbreast densityen
dc.subjectClassification accuracyen
dc.subjectEdge patternsen
dc.subjectFibroglandular tissueen
dc.subjectFilter responseen
dc.subjectImage noiseen
dc.subjectLocal Binary Patternsen
dc.subjectLocal featureen
dc.subjectScale Invariant Feature Transformsen
dc.subjectSift descriptoren
dc.subjectTexture characterizationsen
dc.subjectTexture descriptorsen
dc.titleCombination of different texture features for mammographic breast density classificationen
dc.description.endingpage737 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: IEEEen
dc.description.notesIEEE Computer Societyen
dc.description.notesUniversity of Cyprusen
dc.description.notesBiological and AI Foundation (BAIF)en
dc.description.notesFrederick Universityen
dc.description.notesConference code: 95206en
dc.description.notesCited By :9</p>en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]

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