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Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images
by
Choi, Hyunho
, Jeong, Jechang
in
Algorithms
/ Decomposition
/ Discrete Wavelet Transform
/ Domains
/ Image compression
/ Image filters
/ Methods
/ Noise
/ Noise reduction
/ Partial differential equations
/ Physicians
/ Tomography
/ Ultrasonic imaging
/ Ultrasound
/ Wavelet transforms
2020
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Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images
by
Choi, Hyunho
, Jeong, Jechang
in
Algorithms
/ Decomposition
/ Discrete Wavelet Transform
/ Domains
/ Image compression
/ Image filters
/ Methods
/ Noise
/ Noise reduction
/ Partial differential equations
/ Physicians
/ Tomography
/ Ultrasonic imaging
/ Ultrasound
/ Wavelet transforms
2020
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Do you wish to request the book?
Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images
by
Choi, Hyunho
, Jeong, Jechang
in
Algorithms
/ Decomposition
/ Discrete Wavelet Transform
/ Domains
/ Image compression
/ Image filters
/ Methods
/ Noise
/ Noise reduction
/ Partial differential equations
/ Physicians
/ Tomography
/ Ultrasonic imaging
/ Ultrasound
/ Wavelet transforms
2020
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Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images
Journal Article
Despeckling Algorithm for Removing Speckle Noise from Ultrasound Images
2020
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Overview
Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transform (DWT) using symmetry characteristics, weighted guided image filtering (WGIF), and gradient domain guided image filtering (GDGIF). The SRAD filter is exploited as a preprocessing filter because it can be directly applied to a medical US image containing speckle noise without a log-compression. The wavelet domain has the advantage of suppressing the additive noise. Therefore, a homomorphic transformation is utilized to convert the multiplicative noise into additive noise. After two-level DWT decomposition is applied, to suppress the residual noise of an SRAD filtered image, GDGIF and WGIF are exploited to reduce noise from seven high-frequency sub-band images and one low-frequency sub-band image, respectively. Finally, a noise-free image is attained through inverse DWT and an exponential transform. The proposed algorithm exhibits excellent speckle noise elimination and edge conservation as compared with conventional denoising methods.
Publisher
MDPI AG
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