In this study we present an automatic algorithm for the detection and functional assessment of lung nodules on three-dimensional slices derived from a hybrid PET/CT scanner. In addition to differentiate malignant from benign lesions, the algorithm was mainly designed for assessing the response of lung cancer to therapy. The automated algorithm involves three major steps. First, the lung region is segmented from low resolution multislice CT images. Once the lung is segmented on CT images, a search of seed pixels with maximum activity of 18FDG is undertaken into the lung regions of the electronically registered PET images. A 3D growing algorithm identified the lesion pixels around the maximum 18FDG activity seed pixels. In the third step, the total activity (Bq), concentration (Bq/ml), metabolically active volume (ml) and standard uptake values (SUV) were calculated for lesions on PET images. A threshold and filtering method was applied to high resolution CT scans to determine the CT volume of these lesions identified on PET images. All PET images were corrected for attenuation and partial volume effect and cross calibrated with a standard activity measured in a dose calibrator. Studies were performed using a hybrid PET/CT Discovery LS (GE Medical Systems). The feasibility and robustness of the automatic algorithm was demonstrated in studies with a lung-chest phantom and by retrospective analysis of clinical studies.