Segmentation of cerebral edema from computed tomography (CT) scans for patients with intracranial hemorrhage (ICH) is challenging as edema does not show clear boundary on CT. By exploiting the clear boundary on T2-weighted magnetic resonance images, a method was proposed to segment edema on CT images through the model learned from 14 patients with both CT and T2-weighted images using ground truth edema from T2-weighted images to train and classify the features extracted on CT images. By constructing negative samples around the positive samples, employing the feature selection based on common subspace measures, and using support vector machine, the classification model was attained corresponding to the optimum segmentation accuracy. The method has been validated against 36 clinical head CT scans presenting ICH to yield a mean Dice coefficient of 0.859±0.037, which is significantly higher than that of region growing method (0.789±0.036, P＜0.000 1), semi-automated level set method (0.712±0.118, P＜0.000 1), and threshold based method (0.649±0.147, P＜0.000 1). Comparative experiments have been carried out to find that the classifier purely from CT will yield a significantly lower Dice coefficient (0.686±0.136, P＜0.000 1). The higher segmentation accuracy may suggest that clear boundaries of edema from T2-weighted images provide implicit constraints on CT images that could differentiate edema from its neighboring brain tissues more accurately. The proposed method could provide a potential tool to quantify edema, evaluate the severity of pathological changes, and guide therapy of patients with ICH.