Accurate lung parenchyma segmentation plays an important role in lung disease diagnosis, which contributes to improving the survival rate and prognostic conditions. However, image noises, complex thorax tissue structures, large individual differences, and so on make lung segmentation a complex task. In this paper, an automatic lung parenchyma segmentation algorithm based on superpixels and matrix degree of grey incidences is presented to address the problem. Lung CT image is first preprocessed with a group of morphological operations and then divided into a set of superpixels. Then, matrix grey incidence is utilized to classify the superpixels of the thorax into lung tissues and pleural tissues after the reference superpixels were extracted. Finally, the segmentation results are refined with a contour correction approach based on corner detection and convex hull to facilitate accurate lung contours. The segmentation results of our algorithm are compared with ground truths, and experimental results show that the proposed algorithm achieves high accuracy, and the average Jaccard's similarity index is more than 92%.