Bayesian wavelet snake for computer-aided diagnosis of lung nodules

Abstract

An edge-guided active contour based on the wavelet transform called the Bayesian wavelet snake has been developed for identifying a closed-contour object with a fuzzy and low-contrast boundary. The wavelet snake is designed to deform its shape based on a maximum a posteriori estimate calculated by the fast wavelet transform. Our new method was applied to a computer-aided diagnosis scheme for detection of pulmonary nodules in digital chest radiographs. In this scheme, a filter based on the edge gradient was employed for enhancement of nodules, followed by creation of multiscale edges by spline wavelets for extraction of portions of the boundary of a candidate nodule. These multiscale edges are then used to “guide” the wavelet snake for estimation of the boundary of the nodule. The degree of overlap between the resulting snake and the multiscale edges was used as a feature for distinguishing nodules from false-positive detections that consist of only normal anatomic structures. The wavelet snake was combined with morphological features by means of an artificial neural network for further reduction of false detections. The performance of our scheme was evaluated by receiver operating characteristic analysis based on a publicly available database of chest radiographs.