A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
Digital image processing
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Automatic extraction of roads from aerial images based on scale space and snakes
Machine Vision and Applications
Handbook of medical imaging
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Robot Vision
Digital Picture Processing
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Combined morphological-spectral unsupervised image segmentation
IEEE Transactions on Image Processing
Expert Systems with Applications: An International Journal
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This paper presents a novel edge detection algorithm, using Haar wavelet transform and signal registration. The proposed algorithm has two stages: (a) adaptive edge detection with the maximum entropy thresholding technique on time-scale plane and (b) edge linkage into a contour line with signal registration in order to close edge discontinuities and calculate a confidence index for contour linkages. This index measures the level of confidence in the linkage of two adjacent points in the contour structure. Experimenting with synthetic images, we found out the lower level of confidence can be set to approximately e^-^2. The method was tested on 200 synthetic images at different signal-to-noise ratios (SNRs) and 11 clinical images. We assessed its reliability, accuracy and robustness using the mean absolute distance (MAD) metric and our confidence index. The results for MAD on synthetic images yield the mean of 0.7 points and standard deviation (std) of 0.14, while the mean confidence level is 0.48 with std of 0.19 (the values are averaged over SNRs from 3 to 50dB each in 20 Monte-Carlo runs). Our assessment on clinical images, where the references were expert's annotations, give MAD equal 1.36+/-0.36 (mean+/-std) and confidence level equal 0.67+/-0.25 (mean+/-std).