Combined edge detection using wavelet transform and signal registration
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 experts annotations, give MAD equal 1.360.36 (meanstd) and confidence level equal 0.670.25 (meanstd).
edge detection wavelet transform signal registration edge model