It is common sense that CAD has great significance in the lung nodule detection. But it is still controversial whether the CAD can also automatically differentiates between malignant and benign pulmonary nodules. The primary cause of this controversy is due to the subjective definition of 9 characteristics of nodules which are important basis of nodule identification. In other word, these characteristics are too dependent on the doctor scoring, and no objective standard of them has built which make these characteristics can be obtained by calculation.The main aim of this paper is to establish a quantitative method of the characteristics and refine these nine characteristics. This new method is used to find the objective replacement (a series features which can be measured through algorithms) of these subjective characteristics of the pulmonary nodule detection with Bayesian analysis.The experiment of our method proves that it is feasible to substitute the features of Pulmonary Nodule obtained by calculating for the characteristics of the nodule which only used to be gotten by the subjective judgment of doctors.