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Objective Automatic lung parenchyma segmentation is one of the most important steps in the computer aided diagnosis (CAD) of the lung. To increase segmentation speed, an algorithm based on resampling of the image data is proposed and implemented. Methods The algorithm firstly resamples and extracts a small part (1/8) of the original CT images data. Several steps are implemented to get preliminary segmentation with the resampled data, which include simple threshold segmentation, body region elimination, trachea extraction, removal of interior cavities, left-right lung separation and lung nodule filling. The final results are obtained after projecting the preliminary segmentation to the original dataset and morphology smoothing. The proposed algorithm is applied to 20 patients’ data (2556 slices), and the results are compared to the manual segmentations. Results The algorithm can get accurate results with an average area overlapped ratio 99.02% to the manual segmentation by the radiologist, and works well for the abnormal cases (right-left connected, with nodules and uncompleted views). Through resampling, the time consumption of the algorithm is shortened significantly, typically by 50%, and the processing for one slice image is less than 0.25 s. Conclusions The proposed automatic lung parenchyma segmentation algorithm with excellent robustness and high speed, can get accurate result and satisfy the requirements of current clinical applications.