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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.

Background-Aim: Mammographically dense breast tissue is related to a higher risk of breast cancer. We aim to evaluate a computerized system, assess whether it can provide an accurate and objective estimation of the breast density and if it can accurately classify the mammograms according to the ACR/ BIRADS system. Methods: We retrospectively reviewed the mediolateral oblique (MLO) and cranial-caudal (CC) views of 83 normal mammograms and classified them, both manually and with the use of computerized texture analysis (CTA), according to their density. We grouped the mammograms either into two (ACR 1-2, ACR 3-4) or four categories (ACR 1 to 4). An inter-rater reliability analysis was performed using the kappa statistic to determine consistency among the radiologist and the CTA. Results: The best matching was observed for the MLO view when the classification involved 2 groups (94%).