The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available. (C) 2014 Elsevier Ireland Ltd. All rights reserved. ; Gent perGent Fund (EDEMAC Project) ; Spain’s Health Research Fund (Fondo de Investigación Santiaria) (PI060386) ; Spain’s Health Research Fund (Fondo de Investigación Santiaria) (FIS PS09/00790) ; Spanish MICINN grants TIN2009-14205-C04-02 ; Consolider Ingenio 2010: MIPRCV (CSD2007-00018) ; Spanish Federationof Breast Cancer Patients (Federación Española de Cáncer de Mama) (FECMA 485 EPY 1170-10) ; Universitat Politècnica de València ; Llobet Azpitarte, R.; Pollán, M.; Antón Guirao, J.; Miranda-García, J.; Casals El Busto, M.; Martinez Gomez, I.; Ruiz Perales, F. (2014). Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction. Computer Methods and Programs in Biomedicine. 116(2):105-115. doi:10.1016/j.cmpb.2014.01.021. ; Senia ; 105 ; 115 ; 116 ; 2