Our group developed the computer aided diagnosis (CAD) system for lung cancer in 1996, and has been used in clinical field since 1997. From this CAD system (conventional system), we discovered problem and we attempted to solve the problem by using our proposed algorithm. The proposed algorithm succeeded in the improvement of the following three problems of the conventional system. (1) Weak extraction algorithm of region of interest (ROI) with noise, (2) Poor knowledge of chest structure, and (3) diagnostic processing for nodule of limited size. In this paper, the algorithm that solves problem (2) and (3) is described. We evaluated the proposed algorithm, which was applied to the following four databases. (A) Lung cancer database, (B) detailed examination database, (C) a large-scale screening database by 10mm-thickness images reconstructed from single-slice CT scan, and (D) a large-scale screening database by 10mm-thickness images reconstructed from multi-slice CT scan. The proposed method obtained the following successful results: Lung cancer database 95.7% TP and detailed examination 94.8% TP. For the large-scale screening database, we evaluated each examination process from physicians’ reading to cancer decision. The extraction rate of proposed algorithm improved as the examinations proceed. Two false positive results were obtained. False positive 1 (6.8-9.2 shadows/case) needed for a detailed examination and the object of false positive 2 (2.6-4.0 shadows/case) was an abnormal shadow.