Computer-aided diagnosis (CAD) system can detect, segment and diagnose pulmonary nodules from CT images, and improve the survival rate of early lung cancer, which has important clinical significance. As the appearance of pulmonary nodules varies with its type, size, location, internal structure, and malignancy, nodule detection and diagnosis have become a major challenge . Here the key techniques and challenges are analyzed in four main processing stages: segmentation of lungs from chest images, detection of pulmonary nodules inside the lung fields, pulmonary nodule segmentation, and diagnosis of pulmonary nodules as benign or malignant. Further research is needed to optimize the diagnosis algorithm sensitivity of nodules with different sizes and shapes, thus decreasing the number of false positives, and improving automation level of diagnosis. Finally, the picture archiving and communication systems (PACS) should be integrated with electronic medical record systems (EMRS) in order to be adopted in clinical practice. 