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. Due to the amount of data it produces, however, automating the nodule detection process is viable. The challenging problem for any nodule detection system is to keep low false-positive detection rate while maintaining high sensitivity. In this paper, we first describe a 3D filter-based method for pulmonary micronodule detection from high-resolution 3D chest CT images. Then, we propose a false-positive elimination method based on a deformable model. Finally, we present promising results of applying our method to. various clinical chest CT datasets with over 90% detection rate. The proposed method focuses on the automatic detection of both calcified (high-contrast) and noncalcified (low-contrast) granulomatous nodules less than 5mm in diameter.