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The main purpose of pulmonary nodule detection is to classify nodule from the lung computed tomography (CT) images. The variability of class is mainly expected to the grey-level variance, texture differences and shape. The purpose of this study is to develop a nodule detector based on Two-dimensional Principal Component Analysis (2DPCA). We extract the features using 2DPCA from nodule candidate images. Nodule candidates are then classified using threshold. The proposed method significantly reduces false positive (FP) rate. We applied it to Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The experimental results show the effectiveness and efficiency of the proposed method. The proposed method achieved 80.60% detection rate with 0.0391 FPs per slice. [ABSTRACT FROM AUTHOR]