Abstract
BackgroundIn 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. We propose a multiple classifier system (MCS) to classify breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) combining morphological features and dynamic information.MethodsThe proposed MCS combines the results of two classifiers trained with dynamic and morphological features separately. Twenty-six malignant and 22 benign breast lesions, histologically proven, were analysed. The lesions were subdivided into two groups: training set (14 benign and 18 malignant) and testing set (8 benign and 8 malignant). Volumes of interest were extracted both manually and automatically. We initially considered a feature set including 54 morphological features and 98 dynamic features. These were reduced by means of a selection procedure to delete redundant parameters. The performance of each of the two classifiers and of the overall MCS was compared with pathological classification.ResultsWe obtained an accuracy of 91.7% on the testing set using automatic segmentation and combining the best classifier for morphological features (decision tree) and for dynamic information (Bayesian classifier). With implementation of the MCS, an increase in accuracy of 12.5% and of 31.3% was obtained compared with the accuracy of the Bayesian classifier tested with dynamic features and with that of the decision tree tested with morphological parameters, respectively.ConclusionsAn MCS can optimise the accuracy for breast lesion classification combining morphological features and dynamic information.
Highlights
Breast cancer is the most common cancer among women in the western world
Morphological and dynamic features According to our previous studies [18,19,20], we considered a feature set including 54 morphological features and 98 dynamic features
volume of interests (VOIs) classification The proposed multiple classifier system (MCS) combines the results of two classifiers trained separately with dynamic and morphological features, respectively (Fig. 2)
Summary
Breast cancer is the most common cancer among women in the western world. To date it is the second leading cause of cancer death in women (after lung cancer) and is estimated to cause 15% of cancer deaths [1]. The currently most widespread screening method is X-ray mammography [2]. This method is not adequate for young women in the presence of dense. Detection and characterisation of breast lesions on mammography is difficult because of the lack of functional information. We propose a multiple classifier system (MCS) to classify breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) combining morphological features and dynamic information
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