A computer aided detection system for cerebral microbleeds in brain MRI

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. In this paper, we propose a computer aided detection (CAD) system for the detection of CMBs to speed up visual analysis. Our method consists of three steps: (i) skull-stripping (ii) initial candidate selection and (iii) reduction of false-positives (FPs) using a two layer classification. Geometrical, intensity-based and local image descriptor features were used in the classification steps. The training and test set consist of 156 subjects (448 CMBs) and 81 subjects (183 CMBs), respectively. The sensitivity for CMB detection was 91% with, on average, 4.1 false-positives per subject.