A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms

Abstract

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 class. If the test ROI is predicted as other class, it is inputted to second classifier for the classification into B-II/dense class. If the test ROI is predicted as belonging to dense class, it is inputted to classifier for the classification into B-III/B-IV class. In this work five hierarchical classifiers designs consisting of 3 PCA-kNN, 3 PCA-PNN, 3 PCA-ANN, 3 PCA-NFC and 3 PCA-SVM classifiers has been proposed. The obtained maximum OCA value is 80.4% using PCA-NFC in hierarchical approach. Further, the best performing individual classifiers are clubbed together in a hierarchical framework to design hybrid hierarchical framework for classification of breast density using digitized screen film mammograms. The proposed hybrid hierarchical framework yields the OCA value of 84.1%. The result achieved by the proposed hybrid hierarchical framework is quite promising and can be used in clinical environment for differentiation between different breast density patterns.