Automated segmentation of brain lesions by combining intensity and spatial information

Quantitative analysis of brain lesions in large clinical trials is becoming more and more important. We present a new automated method, that combines intensity based lesion segmentation with a false positive elimination method based on the spatial distribution of lesions. A Support Vector Regressor (SVR) is trained on expert-defined lesion masks using image histograms as features, in order to obtain an initial lesion segmentation. A lesion probability map that represents the spatial distribution of true and false positives on the intensity based segmentation is constructed using the segmented lesions and manual masks. A k-Nearest Neighbor (kNN) classifier based on the lesion probability map is applied to refine the segmentation.