Advances in MR technology have improved the potential for visualization of small lesions in brain images. This has resulted in the opportunity to detect cerebral microbleeds (CMBs), small hemorrhages in the brain that are known to be associated with risk of ischemic stroke and intracerebral bleeding. Currently, no computerized method is available for fully- or semi-automated detection of CMBs. In this paper, we propose a CAD system for the detection of CMBs to speed up visual analysis in population-based studies. Our method consists of three steps: (i) skull-stripping (ii) initial candidate selection (iii) reduction of false-positives using a two layer classi cation and (iv) determining the anatomical location of CMBs. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The geometrical, intensity-based and local image descriptor features were used in the classi cation steps. The training and test sets consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The sensitivity for CMB detection was 90% with, on average, 4 false-positives per subject.