A wavelet-based image matching method was developed for removal of normal anatomic structures in chest radiographs for reduction of false positives reported by our computer- aided diagnosis (CAD) scheme for detection of lung nodules. In our approach, two regions of interest (ROIs) are extracted, one from the position where a candidate of a nodule is located, and the other from the position located at a point symmetric to the first position relative to the spine. The second ROI contains normal anatomic structures similar to those of the first ROI. A non-linear functional representing the squared differences between the two images is formulated, and is minimized by a coarse-to-fine approach to yield a planar mapping that matches the two similar images. A smoothing term is added to the non-linear functional, which penalizes discontinuous and irregular mappings. If no structure remains in the difference between these matched images, then the first ROI is identified to be a false detection (i.e., it contains only normal structures); otherwise, it is regarded as a nodule (i.e., it contains an abnormal structure). A preliminary result shows that our method is effective in removing normal anatomic structures and thus is useful for substantially reducing the number of false detections in our CAD scheme.