The detection of asymptomatic lacunar infarcts on magnetic resonance (MR) images are important tasks for radiologists to ensure the prevention of sever cerebral infarction. However, their accurate identification is often difficult task. Therefore, the purpose of this study is to develop a computer-aided diagnosis scheme for the detection of lacunar infarcts. Our database consisted of 1,143 T1- and 1,143 T2-weighted images obtained from 132 patients. We first segmented the cerebral region in the T1- weighted image by using a region growing technique. For identifying the initial lacunar infarcts candidates, white top-hat transform and multiple-phase binarization were then applied to the T2- weighted image. For eliminating false positives (FPs), we determined 12 features, i.e., the locations x and y, density differences in the T1- and T2- weighted images, nodular components (NC), and nodular & linear components (NLC) from a scale 1 to 4. The NCs and NLCs were obtained using filter bank technique. The rule-based scheme and a neural network with 12 features were employed as the first step for eliminating FPs. The modular classifier was then used for eliminating three typical sources of FPs. As a result, the sensitivity of the detection of lacunar infarcts was 96.8% with 0.30 FP per image. Our computerized scheme would assist radiologists in identifying lacunar infarcts on MR images.