In this paper, we propose a method for reducing false positives in X-ray CT images using ridge shadow extraction techniques and 3D geometric object models. Suspicious shadows are detected by our variable N-quoit (VNQ) filter, which is a type of mathematical morphology filter. This filter can detect lung cancer shadows with the sensitivity over 95[%], but it also detects many false positives which are mainly related to blood vessel shadows. We have developed two algorithms to distinguish lung nodule shadows from blood vessel shadows. In the first algorithm, the ridge shadows, which come from blood vessels, are emphasized by our Tophat by Partial Reconstruction filter which is also a type of mathematical morphology filter. And then, the region of the ridge shadow is extracted using binary distance transformation. In the second algorithm, we propose a recognition method of nodules using 3D geometric lung nodule and blood vessel models. The anatomical knowledge about the 3D structures of nodules and blood vessels can be reflected in recognition process. By applying our new method to actual CT images (37 patient images), a good result has been acquired.