In this paper we propose a Multiple Classifier System (MCS) for classifying breast lesions in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The proposed MCS combines the results of two classifiers trained with dynamic and morphological features respectively. Twenty-one malignant and seventeen benign breast lesions, histologically proven, were analyzed. Volumes of Interest (VOIs) have been automatically extracted via a segmentation procedure assessed in a previous study. The performance of the MCS have been compared with histological classification. Results indicated that with automatic segmented VOIs 90% of test-set lesions were correctly classified.
A Multiple Classifier System for Classification of Breast Lesions Using Dynamic and Morphological Features in DCE-MRI / Roberta, Fusco; Sansone, Mario; Antonella, Petrillo; Sansone, Carlo. - 7626:(2012), pp. 684-692. (Intervento presentato al convegno SSPR&SPR 2012 - Joint IAPR International Workshop tenutosi a Hiroshima (Japan) nel 7-9 November) [10.1007/978-3-642-34166-3_75].
A Multiple Classifier System for Classification of Breast Lesions Using Dynamic and Morphological Features in DCE-MRI
SANSONE, MARIO;SANSONE, CARLO
2012
Abstract
In this paper we propose a Multiple Classifier System (MCS) for classifying breast lesions in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The proposed MCS combines the results of two classifiers trained with dynamic and morphological features respectively. Twenty-one malignant and seventeen benign breast lesions, histologically proven, were analyzed. Volumes of Interest (VOIs) have been automatically extracted via a segmentation procedure assessed in a previous study. The performance of the MCS have been compared with histological classification. Results indicated that with automatic segmented VOIs 90% of test-set lesions were correctly classified.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
