A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pulse and staircase edge models
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavior of Edges in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Edge detection by scale multiplication in wavelet domain
Pattern Recognition Letters
Scale-adaptive detection and local characterization of edges based on wavelet transform
Signal Processing - Signal processing in communications
A shearlet approach to edge analysis and detection
IEEE Transactions on Image Processing
Image multi-scale edge detection using 3-D hidden Markov model based on the non-decimated wavelet
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
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In this article we suggest a fast multi-scale edge-detection scheme for medical ultrasound signals. The edge-detector is based on well-known properties of the continuous wavelet transform. To achieve both good localization of edges and detect only significant edges, we study the maxima-lines of the wavelet transform. One can obtain the maxima-lines between two scales by computing the wavelet transform at several intermediate scales. To reduce computational effort and time we suggest a time-scale filtering procedure which uses only few scales to connect modulus-maxima across time-scale plane. The design of this procedure is based on a study of maxima-lines corresponding to edges typical for medical ultrasound signals. This study allows us to construct an algorithm for medical ultrasound signals which meets the demand for speed, but not on expense of reliability. The edge-detection algorithm has been applied to a large class of medical ultrasound signals including tumour-, liver- and artery-images. Our results show that the proposed algorithm effectively detects major features in such signals, including edges with low contrast.