Texture analysis of lesion perfusion volumes in dynamic contrast-enhanced breast MRI

Abstract

This study introduces a novel texture analysis scheme applied to perfusion volumes in dynamic contrast-enhanced (DCE) breast MRI to provide a method of lesion discrimination. DCE MRI was applied to 24 lesions (12 malignant, 12 benign). Automatic segmentation was performed for extraction of a lesion volume, which was divided into whole, rim and core volume partitions. Lesion perfusion volumes were classified using three-time-points (3TP) method of computer-aided diagnosis. Receiver operating characteristic curve (ROC) analysis was performed for differentiation of benign and malignant lesions using texture features of perfusion volumes classified by the 3TP method. When using the texture features of perfusion volumes divided into rim and core lesion volume, the texture features to have more improved accuracy appeared than using whole lesion volume. This result suggests that lesion classification using texture features of local perfusion volumes is helpful in selecting meaningful texture features for differentiation of benign and malignant lesions.