Automated lesion detection in dynamic contrast enhanced magnetic resonance imaging of breast

Abstract

We propose an automated method in detecting lesions to assist radiologists in interpreting dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of breast. The aim is to highlight the suspicious regions of interest to reduce the searching time of the lesions and the possibility of radiologists overlooking small regions. In our method, we locate the suspicious regions by applying a threshold on essential features. The features are normalized to reduce the variation between patients. Support vector machine classifier is then applied to exclude normal tissues from these regions, using both kinetic and morphological features extracted in the lesions. In the evaluation of the system on 21 patients with 50 lesions, all lesions were successfully detected with 5.02 false positive regions per breast.