This work presents a computer-aided detection (CAD) system to aid radiologists in finding sclerotic bone metastases in the spine on CT images. The spine is first segmented using thresholding, region growing and a vertebra template. A watershed algorithm and a merging routine segment potential lesion candidates in each two-dimensional (2-D) axial CT image. Next, overlapping 2-D detections on sequential CT slices are merged to form 3-D candidate lesions. For each of these, 30 quantitative features based on shape, density, and location are computed. After a feature filter eliminates clearly false candidates, a ground truth on 10 clinical cases segmented manually by an expert, and the features of each CAD candidate are used to train seven support vector machines. The segmentation algorithm detects 164 out of the 212 manually segmented lesions. A ten-fold cross-validation trained on these detections results in 77.4% sensitivity at an average of 9.44 false positives per case.