Computed Tomography (CT) is generally accepted as the most sensitive way for lung cancer screening. Its high contrast resolution allows the detection of small nodules and, thus, lung cancer at a very early stage. In this paper, we propose a method for automating nodule detection from high-resolution 3D chest CT images. Our method focuses on the detection of both calcified (high-contrast) and noncalcified (low-contrast) granulomatous nodules less than 5mm in diameter, using a series of 3D filters including a filter for vessels and noise suppression, a filter for nodule enhancement, and a filter for false-positive elimination based on local skeletonization of suspicious nodule areas. We also present promising results of applying our method to various clinical chest CT datasets with over 90% detection rate.