Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, an...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical imaging 2006-03, Vol.25 (3), p.297-311
Main Authors: Yong Yue, Croitoru, M.M., Bidani, A., Zwischenberger, J.B., Clark, J.W.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Design methodology
Diffusion
Dyadic wavelet transform
Humans
Image edge detection
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image quality
Imaging, Three-Dimensional - methods
Iterative algorithms
iterative denoising
Iterative methods
Mathematical models
Models, Biological
Models, Statistical
multiscale analysis
Noise reduction
nonlinear diffusion
Nonlinear Dynamics
Nonlinearity
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Speckle
speckle suppression
Studies
Ultrasonic imaging
Ultrasonic testing
Ultrasonography - methods
Ultrasound
ultrasound imaging
Visualization
Wavelet
Wavelet coefficients
wavelet diffusion
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2005.862737