It is important to enhance and detect pulmonary nodules in computed tomography(CT) images in order to assist radiologists in the detection of lung cancer. Nodules which are included in medical image generally have multiple size and scale and have blob-like structure. Recently, 3D multiscale filter approach is proposed for lung nodules detection. However, the 3D method takes too much computing time to be applicable in computer-aided diagnostic (CAD). In this paper, we propose a three step method for lung nodule detection in CT images. Firstly, we use 2D multiscale filter to detect the candidates of lung nodules on the slice images. Secondly, distinguish nodules between non-nodules which have blob-like shape by use of geometrical constraint region growing. Finally, extract shape features of each region and use an automated rule-based classifier for reduction of false positives. The experimental results indicate that our algorithm can achieve a relatively high performance for nodule detection.