A novel approach based on a multiscale edge-guided wavelet snake model is developed for deliniation of pulmonary nodules in digital chest radiographs. The approach is applied to the differentiation of nodules and false positives reported by our computer-aided diagnosis (CAD) scheme for detection of nodules. The wavelet snake is a deformable contour that is designed to identify the boundary of a round object. The shape of the snake is determined by a set of wavelet coefficients in a certain range of scales. Portions of the boundary of a nodule are first extracted by multiscale edge representation. Then the multiscale edges are fitted by deformation of the shape of the snake through a change in the wavelet coefficients by use of a gradient descent algorithm. The degree of overlap between the fitted snake and the multiscale edges is calculated as a measure for classification of nodules and false detections. A total of 242 regions of interest, consisting of 90 nodules and 152 false positives, reported by our existing CAD scheme are used for evaluation of our method by means of receiver operating characteristic (ROC) analysis. The false positives are difficult to distinguish from nodules, because they cannot be removed, even though various methods for false-positive elimination processes are employed in our CAD scheme. Our method based on the multiscale edge-guided snake model yields an area under the ROC curve of 0.74, which can eliminate 15% of false positives with the sacrifice of only one nodule. The result indicates that our method appears to be effective in the classification of nodules and false positives, even when difficult false positives are included.