Connectionist learning procedures
Machine learning: paradigms and methods
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Ten lectures on wavelets
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active vision
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
An Efficient Active Contour Model Through Curvature Scale Space Filtering
Multimedia Tools and Applications
Computer theory and digital image processing applied to brain activation recognition
Integrated Computer-Aided Engineering
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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 {\it 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.