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In the present work, a hybrid hierarchical framework for classification of breast density using digitized film screen mammograms has been proposed. For designing of an efficient classification framework 480 MLO view digitized screen film mammographic images are taken from DDSM dataset. The ROIs of fixed size i.e. 128 × 128 pixels are cropped from the center area of the breast (i.e. the area where glandular ducts are prominent). A total of 292 texture features based on statistical methods, signal processing based methods and transform domain based methods are computed for each ROI. The computed feature vector is subjected to PCA for dimensionality reduction. The reduced feature space is fed to the classification module. In this work 4-class breast density classification has been conducted using hierarchical framework where the first classifier is used to classify an unknown test ROI into B-I/other …