Helical CT scans have shown effectiveness in detecting lung nodules compared with the convention thoracic radiography. However, in a two-dimensional (2-D) image slice, it is difficult to differentiate nodules from the vertically oriented pulmonary blood vessels. This paper reports an object-based deformation method to detect lung nodules from CT images in three-dimension (3-D). Object-based deformation method in this paper consists of preprocessing and nodule detection. CT numbers are used to identify the pulmonary region and the objects of nodules, blood vessels, and airways. Hough transform is used to identify each circle shape within the pulmonary region. The circles in the different slices are then grouped into the same nodule, airway, or blood to be a target object. To differentiate lung nodules from blood vessels and airways, we use a deformable seed object technique. For a given target object within the pulmonary region, the seed object grows within the target object until it is against the wall of the target object. The seed object is then deformed to match the target object. A cost function is used to match the seed object and the target object. Eight patient cases with 18 nodules were included in this study and the average size of the nodules was 2.4 cm approximately.