A multiple classifier system for classification of breast lesions using dynamic and morphological features in DCE-MRI

Authors:
Roberta Fusco;Mario Sansone;Antonella Petrillo;Carlo Sansone
Affiliations:
Department of Biomedical, Electronic and Telecommunication Engineering, University Federico II of Naples, Italy;Department of Biomedical, Electronic and Telecommunication Engineering, University Federico II of Naples, Italy;Department of Diagnostic Imaging, National Cancer Institute of Naples ‘Fondazione Pascale', Italy;Department of Computer and Systems Engineering, University Federico II of Naples, Italy
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

Citing 2
Cited 0

Segmentation and classification of breast tumor using dynamic contrast-enhanced MR images

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Selection of suspicious ROIs in breast DCE-MRI

ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I

Quantified Score

Hi-index	0.00

Visualization

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.