TY - JOUR
T1 - Simultaneous compression and speckle reduction of clinical breast and fetal ultrasound images using rate-fidelity optimized coding
AU - Nemirovsky-Rotman, S.
AU - Friedman, Z.
AU - Fischer, D.
AU - Chernihovsky, A.
AU - Sharbel, K.
AU - Porat, M.
N1 - Publisher Copyright:
© 2020
PY - 2021/2
Y1 - 2021/2
N2 - Medical ultrasound images are inherently noised with speckle noise, which may interfere with Computer Aided Diagnostics (CAD) tasks, such as automatic segmentation. A compression and speckle de-noising method is proposed and tested on real clinical breast and fetal ultrasound images. The proposed algorithm is based on the optimization of quantization coefficients when applying Wavelet representation on the image, where the optimization is held such that a pre-defined mathematical fidelity criterion with respect to a desired de-speckled image is obtained. The proposed algorithm yields effective speckle reduction whilst preserving the edges in the images, with a reduced computational burden compared to other existing state-of-the-art methods, such as Optimal Bayesian Non-Local Means (OBNLM). In addition, the images are simultaneously compressed to a target bit-rate. The proposed algorithm is evaluated using both objective mathematical fidelity criteria (such as Structural Similarity and Edge Preserve) as well as subjective radiologists tests. The experimental results demonstrate the ability of the proposed method to achieve de-speckled images with compression ratios of approximately 30:1, whilst obtaining competitive subjective as well as objective fidelity measures with respect to the desired de-speckled images.
AB - Medical ultrasound images are inherently noised with speckle noise, which may interfere with Computer Aided Diagnostics (CAD) tasks, such as automatic segmentation. A compression and speckle de-noising method is proposed and tested on real clinical breast and fetal ultrasound images. The proposed algorithm is based on the optimization of quantization coefficients when applying Wavelet representation on the image, where the optimization is held such that a pre-defined mathematical fidelity criterion with respect to a desired de-speckled image is obtained. The proposed algorithm yields effective speckle reduction whilst preserving the edges in the images, with a reduced computational burden compared to other existing state-of-the-art methods, such as Optimal Bayesian Non-Local Means (OBNLM). In addition, the images are simultaneously compressed to a target bit-rate. The proposed algorithm is evaluated using both objective mathematical fidelity criteria (such as Structural Similarity and Edge Preserve) as well as subjective radiologists tests. The experimental results demonstrate the ability of the proposed method to achieve de-speckled images with compression ratios of approximately 30:1, whilst obtaining competitive subjective as well as objective fidelity measures with respect to the desired de-speckled images.
KW - De-noising
KW - Image compression
KW - Medical ultrasound images
KW - Speckle noise
UR - http://www.scopus.com/inward/record.url?scp=85092931418&partnerID=8YFLogxK
U2 - 10.1016/j.ultras.2020.106229
DO - 10.1016/j.ultras.2020.106229
M3 - Article
C2 - 33091651
AN - SCOPUS:85092931418
SN - 0041-624X
VL - 110
JO - Ultrasonics
JF - Ultrasonics
M1 - 106229
ER -