dc.contributor.author | Liasis, G. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.contributor.author | Petroudi, Styliani | en |
dc.creator | Liasis, G. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.creator | Petroudi, Styliani | en |
dc.date.accessioned | 2019-11-13T10:40:59Z | |
dc.date.available | 2019-11-13T10:40:59Z | |
dc.date.issued | 2012 | |
dc.identifier.isbn | 978-1-4673-4358-9 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54398 | |
dc.description.abstract | Mammographic 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.source | IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012 | en |
dc.source | 12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872863823&doi=10.1109%2fBIBE.2012.6399758&partnerID=40&md5=a1f3b2046743c8c2a922341af19cb756 | |
dc.subject | Operational definition | en |
dc.subject | Statistical distribution | en |
dc.subject | Classification (of information) | en |
dc.subject | Graphic methods | en |
dc.subject | Bioinformatics | en |
dc.subject | Support vector machines | en |
dc.subject | Textures | en |
dc.subject | Image segmentation | en |
dc.subject | Texture features | en |
dc.subject | SVM classifiers | en |
dc.subject | Medical image analysis | en |
dc.subject | Breast Cancer | en |
dc.subject | breast density | en |
dc.subject | Classification accuracy | en |
dc.subject | Descriptors | en |
dc.subject | Edge patterns | en |
dc.subject | Fibroglandular tissue | en |
dc.subject | Filter response | en |
dc.subject | Image noise | en |
dc.subject | Keypoints | en |
dc.subject | Local Binary Patterns | en |
dc.subject | Local feature | en |
dc.subject | Mammographic | en |
dc.subject | Mammography | en |
dc.subject | Scale Invariant Feature Transforms | en |
dc.subject | Sift descriptor | en |
dc.subject | textons | en |
dc.subject | texture | en |
dc.subject | Texture characterizations | en |
dc.subject | Texture descriptors | en |
dc.title | Combination of different texture features for mammographic breast density classification | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/BIBE.2012.6399758 | |
dc.description.startingpage | 732 | |
dc.description.endingpage | 737 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: IEEE | en |
dc.description.notes | IEEE Computer Society | en |
dc.description.notes | University of Cyprus | en |
dc.description.notes | Biological and AI Foundation (BAIF) | en |
dc.description.notes | Frederick University | en |
dc.description.notes | Conference code: 95206 | en |
dc.description.notes | Cited By :9</p> | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |