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Lung cancer is one of the most fatal disease with high lethality. In general lung cancers are diagnosed by radiologists. But checking radiological image is a very toilsome work for radiologists because it requires long time practice and high concentration. So, many computer-aided diagnosis (CAD) systems were introduced to cooperate with radiologists and nowadays lots of CAD systems based upon deep learning exceed human experts in diagnosing accuracy. And the remarkable thing is that the much of progress has been made in designing architectures. But, in this paper, a new pre-processing method (lung-range-standardization) is proposed in order to improve the general accuracy of lung-related diagnosis systems and to increase the utility of LIDC dataset. And the efficiency of the proposed pre-processing method is validated through comparison between the nodule segmentation model trained using lung-range-standardization and the nodule segmentation model, which is trained without lung-range-standardization.