Segmentation and classification of breast lesions using dynamic and textural features in Dynamic Contrast Enhanced-Magnetic Resonance Imaging

Abstract

The aim of this study is to propose an approach, based on Multi Layer Perceptron classification of dynamic and textural features, for breast lesions segmentation and classification using Dynamic Contrast Enhanced-Magnetic Resonance Imaging data. We compared the performance obtainable with dynamic, textural and spatio-temporal features. In particular, 98 dynamic features, 60 textural features and 72 spatio-temporal features were considered. The dataset included 20 breast lesions, 10 benign and 10 malignant. The performance of lesion segmentation have been evaluated with respect to manual segmentation provided by an expert radiologist. Results of lesion classification were compared to histological findings. Our results indicate that Multi Layer Perceptron can achieve better results in terms of sensitivity, specificity and accuracy when dynamic features are considered both for lesion segmentation and classification (accuracy of 91 % and 70 %, respectively).