Abstract
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 (95.00 ± 0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01.
Original language | English |
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Article number | 76 |
Journal | Journal of Imaging |
Volume | 5 |
Issue number | 9 |
DOIs | |
Publication status | Published - 12 Sept 2019 |
Keywords
- digital mammogram
- microcalcification
- stack generalization
- classification
- morphological features
- Morphological features
- Microcalcification
- Stack generalization
- Classification
- Digital mammogram