Abstract: In the computer-aided detection (CAD) for lung, the recognition of solitary pulmonary nodules may be interrupted by noise, trachea, bronchial or veins. A method is therefore proposed to recognize lung nodule by integrating the feature weighted fuzzy C-means clustering with pixel spatial information so as to segment the region of interest (ROI). Every feature weight in ROI is calculated by the feature selection algorithm, and the weighted fuzzy C-means clustering algorithm is used to classify ROI, thus recognizing the lung nodules. Experimental results showed that the ROI segmentation algorithm is capable of denoising robustly and that the accuracy of ROI classification is improved. The integrated algorithm proposed has a high sensitivity to tumors with low undetected rate. It can provide helpful information to quickly identify suspicious focus in early stage of lung cancer.