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Simultaneous compression and speckle reduction of clinical breast and fetal ultrasound images using rate-fidelity optimized coding.

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Title: Simultaneous compression and speckle reduction of clinical breast and fetal ultrasound images using rate-fidelity optimized coding.
Authors: Nemirovsky-Rotman, S.1 (AUTHOR) nshira@campus.technion.ac.il, Friedman, Z.2 (AUTHOR), Fischer, D.3 (AUTHOR), Chernihovsky, A.3 (AUTHOR), Sharbel, K.3 (AUTHOR), Porat, M.1 (AUTHOR)
Source: Ultrasonics. Feb2021, Vol. 110, pN.PAG-N.PAG. 1p.
Subjects: Ultrasonic imaging, Breast ultrasound, Fetal imaging, Computer-assisted image analysis (Medicine), Speckle interference, Fetal ultrasonic imaging
Abstract: • A method for compression and de-speckling of medical ultrasound images is proposed. • De-speckling is important for medical diagnostics, but should be carefully applied. • Compression is important in order to meet storage and transmission requirements. • The algorithm is based on optimization of parameter sets in rate-fidelity domain. • Experiments on clinical breast and fetal images demonstrate the method performance. 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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• A method for compression and de-speckling of medical ultrasound images is proposed. • De-speckling is important for medical diagnostics, but should be carefully applied. • Compression is important in order to meet storage and transmission requirements. • The algorithm is based on optimization of parameter sets in rate-fidelity domain. • Experiments on clinical breast and fetal images demonstrate the method performance. 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. [ABSTRACT FROM AUTHOR]
ISSN:0041624X
DOI:10.1016/j.ultras.2020.106229