Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier †

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ([Formula: see text] %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A [Formula: see text] value equal to [Formula: see text].