|Journal||Journal of Imaging|
|Early online date||12 Sep 2019|
|Publication status||E-pub ahead of print - 12 Sep 2019|
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 were carried out to facilitate the feature extraction from segmented microcalcification. A combination ofmorphological, texture, and distribution features fromindividualMC 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, DDSM, and MIAS. The best classification accuracy (95.00 +/- 0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an Az value equal to 0.97 +/- 0.01.
- digital mammogram, microcalcification, stack generalization, classification, morphological features
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- Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier
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