Automatic Computer Aided Diagnosis of Breast Cancer in Dynamic Contrast Enhanced Magnetic Resonance Images

Automated Computer Aided Diagnosis (CADx) systems have the potential to improve the diagnostic accuracy of radiologists. Most CADx algorithms use features generated from outlined regions to differentiate between benign and malignant lesions. Manually outlining these regions for the purpose of analysis is not viable and therefore an automated segmentation method is essential. Our proposed method uses a trained deep Artificial Neural Network (ANN) to classify overlapping tiles in breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) images as lesion or non-lesion. The classified tiles are then grouped into regions. Additional morphological, kinetic and textural features are computed for each detected region. A cascaded Random Forests Classifier (RFC) classifies the regions as malignant or benign. Our method was tested on a dataset containing 71 malignant, 140 benign, and 316 normal studies. Free-response Receiver Operating Characteristic (FROC) analysis of our method shows 94.4% sensitivity at 0.12 false positive detections per normal study.M.Sc