In breast magnetic resonance imaging (MRI) analysis for lesion detection and classification, radiologists agree that both morphological and dynamic features are important to differentiate benign from malignant lesions. This study proposes a multiple classifier system (MCS) to classify breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) combining morphological features and dynamic information. The data gained through testing showed that an MCS can optimise the accuracy for breast lesion classification combining morphological features and dynamic information.
- An MCS combined two classifiers trained with morphological and dynamic features.
- A decision tree was used for classifying morphological features.
- A Bayesian classifier was used for classifying dynamic features.
- Combining morphologic and dynamic features, 92% accuracy can be obtained.
Authors: Roberta Fusco, Massimiliano Di Marzo, Carlo Sansone, Mario Sansone and Antonella Petrillo