Identification of architectural distortions in mammograms using local binary patterns and radial lengths through an exhaustive evaluation framework

• Published in: Expert systems 2018-08, Vol.35 (4), p.n/a
• Main Authors: Chatzistergos, Sevastianos E., Andreadis, Ioannis, Nikita, Konstantina S.
• Format: Article
• Language: English
• Subjects: architectural distortion (AD); Digital imaging; Image analysis; Local Binary Pattern (LBP); mammogram; Online data bases; Performance evaluation; Principal Component Analysis (PCA); Principal components analysis; Screening; Student's t-test; Support vector machines; t test; texture classification
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.12281

A method based on the combination of Local Binary Pattern operator and radial lengths is presented aiming at the identification of Architectural Distortions (ADs) in mammograms. Local Binary Pattern operator, a number of its variants, and radial lengths are combined together producing a high‐dimensional feature space. A process, based on the combination of Principal Component Analysis and ttest, is used to effectively transform feature space and reveal the most descriptive features. The classification step is performed using a Support Vector Machine classifier. Open access databases (Mammographic Image Analysis Society and Digital Database for Screening Mammography) are used through an exhaustive evaluation framework that aims at eliminating both mammogram selection bias and limited subtlety variation, thus enabling a fair and complete comparison procedure. Furthermore, in order to provide a test bed for future comparisons, a dataset is constructed from all the available AD Regions Of Interest in Digital Database for Screening Mammography (163 AD vs 375 Regions Of Interest from specific normal cases) and is used to further evaluate the performance of the proposed method. The method performed flawlessly and classified correctly all cases.